Tag Archives: rescue
#437598 Video Friday: Sarcos Is Developing a New ...
Video Friday is your weekly selection of awesome robotics videos, collected by your Automaton bloggers. We’ll also be posting a weekly calendar of upcoming robotics events for the next few months; here's what we have so far (send us your events!):
IROS 2020 – October 25-29, 2020 – [Online]
ROS World 2020 – November 12, 2020 – [Online]
CYBATHLON 2020 – November 13-14, 2020 – [Online]
ICSR 2020 – November 14-16, 2020 – Golden, Colo., USA
Let us know if you have suggestions for next week, and enjoy today's videos.
NASA’s Origins, Spectral Interpretation, Resource Identification, Security, Regolith Explorer (OSIRIS-REx) spacecraft unfurled its robotic arm Oct. 20, 2020, and in a first for the agency, briefly touched an asteroid to collect dust and pebbles from the surface for delivery to Earth in 2023.
[ NASA ]
New from David Zarrouk’s lab at BGU is AmphiSTAR, which Zarrouk describes as “a kind of a ground-water drone inspired by the cockroaches (sprawling) and by the Basilisk lizard (running over water). The robot hovers due to the collision of its propellers with the water (hydrodynamics not aerodynamics). The robot can crawl and swim at high and low speeds and smoothly transition between the two. It can reach 3.5 m/s on ground and 1.5m/s in water.”
AmphiSTAR will be presented at IROS, starting next week!
[ BGU ]
This is unfortunately not a great video of a video that was taken at a SoftBank Hawks baseball game in Japan last week, but it’s showing an Atlas robot doing an honestly kind of impressive dance routine to support the team.
ロボット応援団に人型ロボット『ATLAS』がアメリカからリモートで緊急参戦！！！
ホークスビジョンの映像をお楽しみ下さい♪#sbhawks #Pepper #spot pic.twitter.com/6aTYn8GGli
— 福岡ソフトバンクホークス（公式） (@HAWKS_official)
October 16, 2020
Editor’s Note: The tweet embed above is not working for some reason—see the video here.
[ SoftBank Hawks ]
Thanks Thomas!
Sarcos is working on a new robot, which looks to be the torso of their powered exoskeleton with the human relocated somewhere else.
[ Sarcos ]
The biggest holiday of the year, International Sloth Day, was on Tuesday! To celebrate, here’s Slothbot!
[ NSF ]
This is one of those simple-seeming tasks that are really difficult for robots.
I love self-resetting training environments.
[ MIT CSAIL ]
The Chiel lab collaborates with engineers at the Center for Biologically Inspired Robotics Research at Case Western Reserve University to design novel worm-like robots that have potential applications in search-and-rescue missions, endoscopic medicine, or other scenarios requiring navigation through narrow spaces.
[ Case Western ]
ANYbotics partnered with Losinger Marazzi to explore ANYmal’s potential of patrolling construction sites to identify and report safety issues. With such a complex environment, only a robot designed to navigate difficult terrain is able to bring digitalization to such a physically demanding industry.
[ ANYbotics ]
Happy 2018 Halloween from Clearpath Robotics!
[ Clearpath ]
Overcoming illumination variance is a critical factor in vision-based navigation. Existing methods tackled this radical illumination variance issue by proposing camera control or high dynamic range (HDR) image fusion. Despite these efforts, we have found that the vision-based approaches still suffer from overcoming darkness. This paper presents real-time image synthesizing from carefully controlled seed low dynamic range (LDR) image, to enable visual simultaneous localization and mapping (SLAM) in an extremely dark environment (less than 10 lux).
[ KAIST ]
What can MoveIt do? Who knows! Let's find out!
[ MoveIt ]
Thanks Dave!
Here we pick a cube from a starting point, manipulate it within the hand, and then put it back. To explore the capabilities of the hand, no sensors were used in this demonstration. The RBO Hand 3 uses soft pneumatic actuators made of silicone. The softness imparts considerable robustness against variations in object pose and size. This lets us design manipulation funnels that work reliably without needing sensor feedback. We take advantage of this reliability to chain these funnels into more complex multi-step manipulation plans.
[ TU Berlin ]
If this was a real solar array, King Louie would have totally cleaned it. Mostly.
[ BYU ]
Autonomous exploration is a fundamental problem for various applications of unmanned aerial vehicles(UAVs). Existing methods, however, were demonstrated to have low efficiency, due to the lack of optimality consideration, conservative motion plans and low decision frequencies. In this paper, we propose FUEL, a hierarchical framework that can support Fast UAV ExpLoration in complex unknown environments.
[ HKUST ]
Countless precise repetitions? This is the perfect task for a robot, thought researchers at the University of Liverpool in the Department of Chemistry, and without further ado they developed an automation solution that can carry out and monitor research tasks, making autonomous decisions about what to do next.
[ Kuka ]
This video shows a demonstration of central results of the SecondHands project. In the context of maintenance and repair tasks, in warehouse environments, the collaborative humanoid robot ARMAR-6 demonstrates a number of cognitive and sensorimotor abilities such as 1) recognition of the need of help based on speech, force, haptics and visual scene and action interpretation, 2) collaborative bimanual manipulation of large objects, 3) compliant mobile manipulation, 4) grasping known and unknown objects and tools, 5) human-robot interaction (object and tool handover) 6) natural dialog and 7) force predictive control.
[ SecondHands ]
In celebration of Ada Lovelace Day, Silicon Valley Robotics hosted a panel of Women in Robotics.
[ Robohub ]
As part of the upcoming virtual IROS conference, HEBI robotics is putting together a tutorial on robotics actuation. While I’m sure HEBI would like you to take a long look at their own actuators, we’ve been assured that no matter what kind of actuators you use, this tutorial will still be informative and useful.
[ YouTube ] via [ HEBI Robotics ]
Thanks Dave!
This week’s UMD Lockheed Martin Robotics Seminar comes from Julie Shah at MIT, on “Enhancing Human Capability with Intelligent Machine Teammates.”
Every team has top performers- people who excel at working in a team to find the right solutions in complex, difficult situations. These top performers include nurses who run hospital floors, emergency response teams, air traffic controllers, and factory line supervisors. While they may outperform the most sophisticated optimization and scheduling algorithms, they cannot often tell us how they do it. Similarly, even when a machine can do the job better than most of us, it can’t explain how. In this talk I share recent work investigating effective ways to blend the unique decision-making strengths of humans and machines. I discuss the development of computational models that enable machines to efficiently infer the mental state of human teammates and thereby collaborate with people in richer, more flexible ways.
[ UMD ]
Matthew Piccoli gives a talk to the UPenn GRASP Lab on “Trading Complexities: Smart Motors and Dumb Vehicles.”
We will discuss my research journey through Penn making the world's smallest, simplest flying vehicles, and in parallel making the most complex brushless motors. What do they have in common? We'll touch on why the quadrotor went from an obscure type of helicopter to the current ubiquitous drone. Finally, we'll get into my life after Penn and what tools I'm creating to further drone and robot designs of the future.
[ UPenn ] Continue reading →
Posted in Human Robots
#437543 This Is How We’ll Engineer Artificial ...
Take a Jeopardy! guess: this body part was once referred to as the “consummation of all perfection as an instrument.”
Answer: “What is the human hand?”
Our hands are insanely complex feats of evolutionary engineering. Densely-packed sensors provide intricate and ultra-sensitive feelings of touch. Dozens of joints synergize to give us remarkable dexterity. A “sixth sense” awareness of where our hands are in space connects them to the mind, making it possible to open a door, pick up a mug, and pour coffee in total darkness based solely on what they feel.
So why can’t robots do the same?
In a new article in Science, Dr. Subramanian Sundaram at Boston and Harvard University argues that it’s high time to rethink robotic touch. Scientists have long dreamed of artificially engineering robotic hands with the same dexterity and feedback that we have. Now, after decades, we’re at the precipice of a breakthrough thanks to two major advances. One, we better understand how touch works in humans. Two, we have the mega computational powerhouse called machine learning to recapitulate biology in silicon.
Robotic hands with a sense of touch—and the AI brain to match it—could overhaul our idea of robots. Rather than charming, if somewhat clumsy, novelties, robots equipped with human-like hands are far more capable of routine tasks—making food, folding laundry—and specialized missions like surgery or rescue. But machines aren’t the only ones to gain. For humans, robotic prosthetic hands equipped with accurate, sensitive, and high-resolution artificial touch is the next giant breakthrough to seamlessly link a biological brain to a mechanical hand.
Here’s what Sundaram laid out to get us to that future.
How Does Touch Work, Anyway?
Let me start with some bad news: reverse engineering the human hand is really hard. It’s jam-packed with over 17,000 sensors tuned to mechanical forces alone, not to mention sensors for temperature and pain. These force “receptors” rely on physical distortions—bending, stretching, curling—to signal to the brain.
The good news? We now have a far clearer picture of how biological touch works. Imagine a coin pressed into your palm. The sensors embedded in the skin, called mechanoreceptors, capture that pressure, and “translate” it into electrical signals. These signals pulse through the nerves on your hand to the spine, and eventually make their way to the brain, where they gets interpreted as “touch.”
At least, that’s the simple version, but one too vague and not particularly useful for recapitulating touch. To get there, we need to zoom in.
The cells on your hand that collect touch signals, called tactile “first order” neurons (enter Star Wars joke) are like upside-down trees. Intricate branches extend from their bodies, buried deep in the skin, to a vast area of the hand. Each neuron has its own little domain called “receptor fields,” although some overlap. Like governors, these neurons manage a semi-dedicated region, so that any signal they transfer to the higher-ups—spinal cord and brain—is actually integrated from multiple sensors across a large distance.
It gets more intricate. The skin itself is a living entity that can regulate its own mechanical senses through hydration. Sweat, for example, softens the skin, which changes how it interacts with surrounding objects. Ever tried putting a glove onto a sweaty hand? It’s far more of a struggle than a dry one, and feels different.
In a way, the hand’s tactile neurons play a game of Morse Code. Through different frequencies of electrical beeps, they’re able to transfer information about an object’s size, texture, weight, and other properties, while also asking the brain for feedback to better control the object.
Biology to Machine
Reworking all of our hands’ greatest features into machines is absolutely daunting. But robots have a leg up—they’re not restricted to biological hardware. Earlier this year, for example, a team from Columbia engineered a “feeling” robotic finger using overlapping light emitters and sensors in a way loosely similar to receptor fields. Distortions in light were then analyzed with deep learning to translate into contact location and force.
Although a radical departure from our own electrical-based system, the Columbia team’s attempt was clearly based on human biology. They’re not alone. “Substantial progress is being made in the creation of soft, stretchable electronic skins,” said Sundaram, many of which can sense forces or pressure, although they’re currently still limited.
What’s promising, however, is the “exciting progress in using visual data,” said Sundaram. Computer vision has gained enormously from ubiquitous cameras and large datasets, making it possible to train powerful but data-hungry algorithms such as deep convolutional neural networks (CNNs).
By piggybacking on their success, we can essentially add “eyes” to robotic hands, a superpower us humans can’t imagine. Even better, CNNs and other classes of algorithms can be readily adopted for processing tactile data. Together, a robotic hand could use its eyes to scan an object, plan its movements for grasp, and use touch for feedback to adjust its grip. Maybe we’ll finally have a robot that easily rescues the phone sadly dropped into a composting toilet. Or something much grander to benefit humanity.
That said, relying too heavily on vision could also be a downfall. Take a robot that scans a wide area of rubble for signs of life during a disaster response. If touch relies on sight, then it would have to keep a continuous line-of-sight in a complex and dynamic setting—something computer vision doesn’t do well in, at least for now.
A Neuromorphic Way Forward
Too Debbie Downer? I got your back! It’s hard to overstate the challenges, but what’s clear is that emerging machine learning tools can tackle data processing challenges. For vision, it’s distilling complex images into “actionable control policies,” said Sundaram. For touch, it’s easy to imagine the same. Couple the two together, and that’s a robotic super-hand in the making.
Going forward, argues Sundaram, we need to closely adhere to how the hand and brain process touch. Hijacking our biological “touch machinery” has already proved useful. In 2019, one team used a nerve-machine interface for amputees to control a robotic arm—the DEKA LUKE arm—and sense what the limb and attached hand were feeling. Pressure on the LUKE arm and hand activated an implanted neural interface, which zapped remaining nerves in a way that the brain processes as touch. When the AI analyzed pressure data similar to biological tactile neurons, the person was able to better identify different objects with their eyes closed.
“Neuromorphic tactile hardware (and software) advances will strongly influence the future of bionic prostheses—a compelling application of robotic hands,” said Sundaram, adding that the next step is to increase the density of sensors.
Two additional themes made the list of progressing towards a cyborg future. One is longevity, in that sensors on a robot need to be able to reliably produce large quantities of high-quality data—something that’s seemingly mundane, but is a practical limitation.
The other is going all-in-one. Rather than just a pressure sensor, we need something that captures the myriad of touch sensations. From feather-light to a heavy punch, from vibrations to temperatures, a tree-like architecture similar to our hands would help organize, integrate, and otherwise process data collected from those sensors.
Just a decade ago, mind-controlled robotics were considered a blue sky, stretch-goal neurotechnological fantasy. We now have a chance to “close the loop,” from thought to movement to touch and back to thought, and make some badass robots along the way.
Image Credit: PublicDomainPictures from Pixabay Continue reading →
Posted in Human Robots
#436984 Robots to the Rescue: How They Can Help ...
As the coronavirus pandemic forces people to keep their distance, could this be robots‘ time to shine? A group of scientists think so, and they’re calling for robots to do the “dull, dirty, and dangerous jobs” of infectious disease management.
Social distancing has emerged as one of the most effective strategies for slowing the spread of COVID-19, but it’s also bringing many jobs to a standstill and severely restricting our daily lives. And unfortunately, the one group that can’t rely on its protective benefits are the medical and emergency services workers we’re relying on to save us.
Robots could be a solution, according to the editorial board of Science Robotics, by helping replace humans in a host of critical tasks, from disinfecting hospitals to collecting patient samples and automating lab tests.
According to the authors, the key areas where robots could help are clinical care, logistics, and reconnaissance, which refers to tasks like identifying the infected or making sure people comply with quarantines or social distancing requirements. Outside of the medical sphere, robots could also help keep the economy and infrastructure going by standing in for humans in factories or vital utilities like waste management or power plants.
When it comes to clinical care, robots can play important roles in disease prevention, diagnosis and screening, and patient care, the researchers say. Robots have already been widely deployed to disinfect hospitals and other public spaces either using UV light that kills bugs or by repurposing agricultural robots and drones to spray disinfectant, reducing the exposure of cleaning staff to potentially contaminated surfaces. They are also being used to carry out crucial deliveries of food and medication without exposing humans.
But they could also play an important role in tracking the disease, say the researchers. Thermal cameras combined with image recognition algorithms are already being used to detect potential cases at places like airports, but incorporating them into mobile robots or drones could greatly expand the coverage of screening programs.
A more complex challenge—but one that could significantly reduce medical workers’ exposure to the virus—would be to design robots that could automate the collection of nasal swabs used to test for COVID-19. Similarly automated blood collection for tests could be of significant help, and researchers are already investigating using ultrasound to help robots locate veins to draw blood from.
Convincing people it’s safe to let a robot stick a swab up their nose or jab a needle in their arm might be a hard sell right now, but a potentially more realistic scenario would be to get robots to carry out laboratory tests on collected samples to reduce exposure to lab technicians. Commercial laboratory automation systems already exist, so this might be a more achievable near-term goal.
Not all solutions need to be automated, though. While autonomous systems will be helpful for reducing the workload of stretched health workers, remote systems can still provide useful distancing. Remote control robotics systems are already becoming increasingly common in the delicate business of surgery, so it would be entirely feasible to create remote systems to carry out more prosaic medical tasks.
Such systems would make it possible for experts to contribute remotely in many different places without having to travel. And robotic systems could combine medical tasks like patient monitoring with equally important social interaction for people who may have been shut off from human contact.
In a teleconference last week Guang-Zhong Yang, a medical roboticist from Carnegie Mellon University and founding editor of Science Robotics, highlighted the importance of including both doctors and patients in the design of these robots to ensure they are safe and effective, but also to make sure people trust them to observe social protocols and not invade their privacy.
But Yang also stressed the importance of putting the pieces in place to enable the rapid development and deployment of solutions. During the 2015 Ebola outbreak, the White House Office of Science and Technology Policy and the National Science Foundation organized workshops to identify where robotics could help deal with epidemics.
But once the threat receded, attention shifted elsewhere, and by the time the next pandemic came around little progress had been made on potential solutions. The result is that it’s unclear how much help robots will really be able to provide to the COVID-19 response.
That means it’s crucial to invest in a sustained research effort into this field, say the paper’s authors, with more funding and multidisciplinary research partnerships between government agencies and industry so that next time around we will be prepared.
“These events are rare and then it’s just that people start to direct their efforts to other applications,” said Yang. “So I think this time we really need to nail it, because without a sustained approach to this history will repeat itself and robots won’t be ready.”
Image Credit: ABB’s YuMi collaborative robot. Image courtesy of ABB Continue reading →
Posted in Human Robots
#436263 Skydio 2 Review: This Is the Drone You ...
Let me begin this review by saying that the Skydio 2 is one of the most impressive robots that I have ever seen. Over the last decade, I’ve spent enough time around robots to have a very good sense of what kinds of things are particularly challenging for them, and to set my expectations accordingly. Those expectations include things like “unstructured environments are basically impossible” and “full autonomy is impractically expensive” and “robot videos rarely reflect reality.”
Skydio’s newest drone is an exception to all of this. It’s able to fly autonomously at speed through complex environments in challenging real-world conditions in a way that’s completely effortless and stress-free for the end user, allowing you to capture the kind of video that would be otherwise impossible, even (I’m guessing) for professional drone pilots. When you see this technology in action, it’s (almost) indistinguishable from magic.
Skydio 2 Price
To be clear, the Skydio 2 is not without compromises, and the price of $999 (on pre-order with delivery of the next batch expected in spring of 2020) requires some justification. But the week I’ve had with this drone has left me feeling like its fundamental autonomous capability is so far beyond just about anything that I’ve ever experienced that I’m questioning why I would every fly anything else ever again.
We’ve written extensively about Skydio, beginning in early 2016 when the company posted a video of a prototype drone dodging trees while following a dude on a bike. Even three years ago, Skydio’s tech was way better than anything we’d seen outside of a research lab, and in early 2018, they introduced their first consumer product, the Skydio R1. A little over a year later, Skydio has introduced the Skydio 2, which is smaller, smarter, and much more affordable. Here’s an overview video just to get you caught up:
Skydio sent me a Skydio 2 review unit last week, and while I’m reasonably experienced with drones in general, this is the first time I’ve tried a Skydio drone in person. I had a pretty good idea what to expect, and I was absolutely blown away. Like, I was giggling to myself while running through the woods as the drone zoomed around, deftly avoiding trees and keeping me in sight. Robots aren’t supposed to be this good.
A week is really not enough time to explore everything that the Skydio can do, especially Thanksgiving week in Washington, D.C. (a no-fly zone) in early winter. But I found a nearby state park in which I could legally and safely fly the drone, and I did my best to put the Skydio 2 through its paces.
Note: Throughout this review, we’ve got a bunch of GIFs to help illustrate different features of the drone. To fit them all in, these GIFs had to be heavily compressed. Underneath each GIF is a timestamped link to this YouTube video (also available at the bottom of the post), which you can click on to see the an extended cut of the original 4K 30 fps footage. And there’s a bunch of interesting extra video in there as well.
Skydio 2 Specs
Photo: Evan Ackerman/IEEE Spectrum
The Skydio 2 is primarily made out of magnesium, which (while light) is both heavier and more rigid and durable than plastic. The offset props (the back pair are above the body, and the front pair are below) are necessary to maintain the field of view of the navigation cameras.
The Skydio 2 both looks and feels like a well-designed and carefully thought-out drone. It’s solid, and a little on the heavy side as far as drones go—it’s primarily made out of magnesium, which (while light) is both heavier and more rigid and durable than plastic. The blue and black color scheme is far more attractive than you typically see with drones.
Photo: Evan Ackerman/IEEE Spectrum
To detect and avoid obstacles, the Skydio 2 uses an array of six 4K hemispherical cameras that feed data into an NVIDIA Jetson TX2 at 30 fps, with the drone processing a million points in 3D space per second to plan the safest path.
The Skydio 2 is built around an array of six hemispherical obstacle-avoidance cameras and the NVIDIA Jetson TX2 computing module that they’re connected to. This defines the placement of the gimbal, the motors and props, and the battery, since all of this stuff has to be as much as possible out of the view of the cameras in order for the drone to effectively avoid obstacles in any direction.
Without the bottom-mounted battery attached, the drone is quite flat. The offset props (the back pair are above the body, and the front pair are below) are necessary to maintain the field of view of the obstacle-avoidance cameras. These hemispherical cameras are on the end of each of the prop arms as well as above and below the body of the drone. They look awfully exposed, even though each is protected from ground contact by a little fin. You need to make sure these cameras are clean and smudge-free, and Skydio includes a cleaning cloth for this purpose. Underneath the drone there are slots for microSD cards, one for recording from the camera and a second one that the drone uses to store data. The attention to detail extends to the SD card insertion, which has a sloped channel that guides the card securely into its slot.
Once you snap the battery in, the drone goes from looking streamlined to looking a little chubby. Relative to other drones, the battery almost seems like an afterthought, like Skydio designed the drone and then remembered, “oops we have to add a battery somewhere, let’s just kludge it onto the bottom.” But again, the reason for this is to leave room inside the body for the NVIDIA TX2, while making sure that the battery stays out of view of the obstacle avoidance cameras.
The magnetic latching system for the battery is both solid and satisfying. I’m not sure why it’s necessary, strictly speaking, but I do like it, and it doesn’t seem like the battery will fly off even during the most aggressive maneuvers. Each battery includes an LED array that will display its charge level in 25 percent increments, as well as a button that you push to turn the drone on and off. Charging takes place via a USB-C port in the top of the drone, which I don’t like, because it means that the batteries can’t be charged on their own (like the Parrot Anafi’s battery), and that you can’t charge one battery while flying with another, like basically every other drone ever. A separate battery charger that will charge two at once is available from Skydio for an eyebrow-raising $129.
I appreciate that all of Skydio’s stuff (batteries, controller, and beacon) charges via USB-C, though. The included USB-C adapter with its beefy cable will output at up to 65 watts, which’ll charge a mostly depleted battery in under an hour. The drone turns itself on while charging, which seems unnecessary.
Photo: Evan Ackerman/IEEE Spectrum
The Skydio 2 is not foldable, making it not nearly as easy to transport as some other drones. But it does come with a nice case that mitigates this issue somewhat, and the drone plus two batteries end up as a passably flat package about the size of a laptop case.
The most obvious compromise that Skydio made with the Skydio 2 is that the drone is not foldable. Skydio CEO Adam Bry told us that adding folding joints to the arms of the Skydio 2 would have made calibrating all six cameras a nightmare and significantly impacted performance. This makes complete sense, of course, but it does mean that the Skydio 2 is not nearly as easy to transport as some other drones.
Photo: Evan Ackerman/IEEE Spectrum
Folded and unfolded: The Skydio 2 compared to the Parrot Anafi (upper left) and the DJI Mavic Pro (upper right).
The Skydio 2 does come with a very nice case that mitigates this issue somewhat, and the drone plus two batteries end up as a passably flat package about the size of a laptop case. Still, it’s just not as convenient to toss into a backpack as my Anafi, although the Mavic Mini might be even more portable.
Photo: Evan Ackerman/IEEE Spectrum
While the Skydio 2’s case is relatively compact, the non-foldable drone is overall a significantly larger package than the Parrot Anafi.
The design of the drone leads to some other compromises as well. Since landing gear would, I assume, occlude the camera system, the drone lands directly on the bottom of its battery pack, which has a slightly rubberized pad about the size of a playing card. This does’t feel particularly stable unless you end up on a very flat surface, and made me concerned for the exposed cameras underneath the drone as well as the lower set of props. I’d recommend hand takeoffs and landings—more on those later.
Skydio 2 Camera System
Photo: Evan Ackerman/IEEE Spectrum
The Skydio 2’s primary camera is a Sony IMX577 1/2.3″ 12.3-megapixel CMOS sensor. It’s mounted to a three-axis gimbal and records 4K video at 60 fps, or 1080p video at 120 fps.
The Skydio 2 comes with a three-axis gimbal supporting a 12-megapixel camera, just enough to record 4K video at 60 fps, or 1080p video at 120 fps. Skydio has provided plenty of evidence that its imaging system is at least as good if not better than other drone cameras. Tested against my Mavic Pro and Parrot Anafi, I found no reason to doubt that. To be clear, I didn’t do exhaustive pixel-peeping comparisons between them, you’re just getting my subjective opinion that the Skydio 2 has a totally decent camera that you won’t be disappointed with. I will say that I found the HDR photo function to be not all that great under the few situations in which I tested it—after looking at a few muddy sunset shots, I turned it off and was much happier.
Photo: Evan Ackerman/IEEE Spectrum
The Skydio 2’s 12-megapixel camera is solid, although we weren’t impressed with the HDR option.
The video stabilization is fantastic, to the point where watching the video footage can be underwhelming because it doesn’t reflect the motion of the drone. I almost wish there was a way to change to unstabilized (or less-stabilized) video so that the viewer could get a little more of a wild ride. Or, ideally, there’d be a way for the drone to provide you with a visualization of what it was doing using the data collected by its cameras. That’s probably wishful thinking, though. The drone itself doesn’t record audio because all you’d get would be an annoying buzz, but the app does record audio, so the audio from your phone gets combined with the drone video. Don’t expect great quality, but it’s better than nothing.
Skydio 2 App
The app is very simple compared to every other drone app I’ve tried, and that’s a good thing. Here’s what it looks like:
Image: Skydio
Trackable subjects get a blue “+” sign over them, and if you tap them, the “+” turns into a spinny blue circle. Once you’ve got a subject selected, you can choose from a variety of cinematic skills that the drone will execute while following you.
You get the controls that you need and the information that you need, and nothing else. Manual flight with the on-screen buttons works adequately, and the double-tap to fly function on the phone works surprisingly well, making it easy to direct the drone to a particular spot above the ground.
The settings menus are limited but functional, allowing you to change settings for the camera and a few basic tweaks for controlling the drone. One unique setting to the Skydio 2 is the height floor—since the drone only avoids static obstacles, you can set it to maintain a height of at least 8 feet above the ground while flying autonomously to make sure that if you’re flying around other people, it won’t run into anyone who isn’t absurdly tall and therefore asking for it.
Trackable subjects get a blue “+” sign over them in the app, and if you tap them, the “+” turns into a spinny blue circle. Once you’ve got a subject selected, you can choose from a variety of cinematic skills that the drone will execute while following you, and in addition, you can select “one-shot” skills that involve the drone performing a specific maneuver before returning to the previously selected cinematic skill. For example, you can tell the drone to orbit around you, and then do a “rocket” one-shot where it’ll fly straight up above you (recording the whole time, of course), before returning to its orbiting.
After you’re done flying, you can scroll through your videos and easily clip out excerpts from them and save them to your phone for sharing. Again, it’s a fairly simple interface without a lot of options. You could call it limited, I guess, but I appreciate that it just does a few things that you care about and otherwise doesn’t clutter itself up.
The real limitation of the app is that it uses Wi-Fi to connect to the Skydio 2, which restricts the range. To fly much beyond a hundred meters or so, you’ll need to use the controller or beacon instead.
Skydio 2 Controller and Beacon
Photo: Evan Ackerman/IEEE Spectrum
While the Skydio 2 controller provides a better hands-on flight experience than with the phone, plus an extended range of up to 3.5 km, more experienced pilots may find manual control a bit frustrating, because the underlying autonomy will supersede your maneuvers when you start getting close to objects.
I was looking forward to using the controller, because with every other drone I’ve had, the precision that a physically controller provides is, I find, mandatory for a good flying experience and to get the photos and videos that you want. With Skydio 2, that’s all out the window. It’s not that the controller is useless or anything, it’s just that because the drone tracks you and avoids obstacles on its own, that level of control precision becomes largely unnecessary.
The controller itself is perfectly fine. It’s a rebranded Parrot Skycontroller3, which is the same as the one that you get with a Parrot Anafi. It’s too bad that the sticks don’t unscrew to make it a little more portable, and overall it’s functional rather than fancy, but it feels good to use and includes a sizeable antenna that makes a significant difference to the range that you get (up to 3.5 kilometers).
You definitely get a better hands-on flight experience with the controller than with the phone, so if you want to (say) zip the drone around some big open space for fun, it’s good for that. And it’s nice to be able to hand the controller to someone who’s never flown a drone before and let them take it for a spin without freaking out about them crashing it the whole time. For more experienced pilots, though, the controller is ultimately just a bit frustrating, because the underlying autonomy will supersede your control when you start getting close to objects, which (again) limits how useful the controller is relative to your phone.
I do still prefer the controller over the phone, but I’m not sure that it’s worth the extra $150, unless you plan to fly the Skydio 2 at very long distances or primarily in manual mode. And honestly, if either of those two things are your top priority, the Skydio 2 is probably not the drone for you.
Photo: Evan Ackerman/IEEE Spectrum
The Skydio 2 beacon uses GPS tracking to help the drone follow you, extending range up to 1.5 km. You can also fly the with the beacon alone, no phone necessary.
The purpose of the beacon, according to Skydio, is to give the drone a way of tracking you if it can’t see you, which can happen, albeit infrequently. My initial impression of the beacon was that it was primarily useful as a range-extending bridge between my phone and the drone. But I accidentally left my phone at home one day (oops) and had to fly the drone with only the beacon, and it was a surprisingly decent experience. The beacon allows for full manual control of a sort—you can tap different buttons to rotate, fly forward, and ascend or descend. This is sufficient for takeoff, landing, to make sure that the drone is looking at you when you engage visual tracking, and to rescue it if it gets trapped somewhere.
The rest of the beacon’s control functions are centered around a few different tracking modes, and with these, it works just about as well as your phone. You have fewer options overall, but all the basic stuff is there with just a few intuitive button clicks, including tracking range and angle. If you’re willing to deal with this relatively minor compromise, it’s nice to not have your phone available for other things rather than being monopolized by the drone.
Skydio 2 In Flight
GIF: Evan Ackerman/IEEE Spectrum
Hand takeoffs are simple and reliable.
Click here for a full resolution clip.
Starting up the Skydio 2 doesn’t require any kind of unusual calibration steps or anything like that. It prefers to be kept still, but you can start it up while holding it, it’ll just take a few seconds longer to tell you that it’s ready to go. While the drone will launch from any flat surface with significant clearance around it (it’ll tell you if it needs more room), the small footprint of the battery means that I was more comfortable hand launching it. This is not a “throw” launch; you just let the drone rest on your palm, tell it to take off, and then stay still while it gets its motors going and then gently lifts off. The lift off is so gentle that you have to be careful not to pull your hand away too soon—I did that once and the drone, being not quite ready, dropped towards the ground, but managed to recover without much drama.
GIF: Evan Ackerman/IEEE Spectrum
Hand landings always look scary, but the Skydio 2 is incredibly gentle. After trying this once, it became the only way I ever landed the drone.
Click here for a full resolution clip.
Catching the drone for landing is perhaps very slightly more dangerous, but not any more difficult. You put the drone above and in front of you facing away, tell it to land in the app or with the beacon, and then put your hand underneath it to grasp it as it slowly descends. It settles delicately and promptly turns itself off. Every drone should land this way. The battery pack provides a good place to grip, although you do have to be mindful of the forward set of props, which (since they’re the pair that are beneath the body of drone) are quite close to your fingers. You’ll certainly be mindful after you catch a blade with your fingers once. Which I did. For the purposes of this review and totally not by accident. No damage, for the record.
Photo: Evan Ackerman/IEEE Spectrum
You won’t be disappointed with the Skydio 2’s in-flight performance, unless you’re looking for a dedicated racing drone.
In normal flight, the Skydio 2 performs as well as you’d expect. It’s stable and manages light to moderate wind without any problems, although I did notice some occasional lateral drifting when the drone should have been in a stationary hover. While the controller gains are adjustable, the Skydio 2 isn’t quite as aggressive in flight as my Mavic Pro on Sport Mode, but again, if you’re looking for a high-speed drone, that’s really not what the Skydio is all about.
The Skydio 2 is substantially louder than my Anafi, although the Anafi is notably quiet for a drone. It’s not annoying to hear (not a high-pitched whine), but you can hear it from a ways away, and farther away than my Mavic Pro. I’m not sure whether that’s because of the absolute volume or the volume plus the pitch. In some ways, this is a feature, since you can hear the drone following you even if you’re not looking at it, you just need to be aware of the noise it makes when you’re flying it around people.
Obstacle Avoidance
The primary reason Skydio 2 is the drone that you want to fly is because of its autonomous subject tracking and obstacle avoidance. Skydio’s PR videos make this capability look almost too good, and since I hadn’t tried out one of their drones before, the first thing I did with it was exactly what you’d expect: attempt to fly it directly into the nearest tree.
GIF: Evan Ackerman/IEEE Spectrum
The Skydio 2 deftly slides around trees and branches. The control inputs here were simple “forward” or “turn,” all obstacle avoidance is autonomous.
Click here for a full resolution clip.
And it just won’t do it. It slows down a bit, and then slides right around one tree after another, going over and under and around branches. I pointed the drone into a forest and just held down “forward” and away it went, without any fuss, effortlessly ducking and weaving its way around. Of course, it wasn’t effortless at all—six 4K cameras were feeding data into the NVIDIA TX2 at 30 fps, and the drone was processing a million points in 3D space per second to plan the safest path while simultaneously taking into account where I wanted it to go. I spent about 10 more minutes doing my level best to crash the drone into anything at all using a flying technique probably best described as “reckless,” but the drone was utterly unfazed. It’s incredible.
What knocked my socks off was telling the drone to pass through treetops—in the clip below, I’m just telling the drone to fly straight down. Watch as it weaves its way through gaps between the branches:
GIF: Evan Ackerman/IEEE Spectrum
The result of parking the Skydio 2 above some trees and holding “down” on the controller is this impressive fully autonomous descent through the branches.
Click here for a full resolution clip.
Here’s one more example, where I sent the drone across a lake and started poking around in a tree. Sometimes the Skydio 2 isn’t sure where you want it to go, and you have to give it a little bit of a nudge in a clear direction, but that’s it.
GIF: Evan Ackerman/IEEE Spectrum
In obstacle-heavy environments, the Skydio 2 prudently slows down, but it can pick its way through almost anything that it can see.
Click here for a full resolution clip.
It’s important to keep in mind that all of the Skydio 2’s intelligence is based on vision. It uses cameras to see the world, which means that it has similar challenges as your eyes do. Specifically, Skydio warns against flying in the following conditions:
Skydio 2 can’t see certain visually challenging obstacles. Do not fly around thin branches, telephone or power lines, ropes, netting, wires, chain link fencing or other objects less than ½ inch in diameter.
Do not fly around transparent surfaces like windows or reflective surfaces like mirrors greater than 60 cm wide.
When the sun is low on the horizon, it can temporarily blind Skydio 2’s cameras depending on the angle of flight. Your drone may be cautious or jerky when flying directly toward the sun.
Basically, if you’d have trouble seeing a thing, or seeing under some specific flight conditions, then the Skydio 2 almost certainly will also. It gets even more problematic when challenging obstacles are combined with challenging flight conditions, which is what I’m pretty sure led to the only near-crash I had with the drone. Here’s a video:
GIF: Evan Ackerman/IEEE Spectrum
Flying around very thin branches and into the sun can cause problems for the Skydio 2’s obstacle avoidance.
Click here for a full resolution clip.
I had the Skydio 2 set to follow me on my bike (more about following and tracking in a bit). It was mid afternoon, but since it’s late fall here in Washington, D.C., the sun doesn’t get much higher than 30 degrees above the horizon. Late fall also means that most of the deciduous trees have lost their leaves, and so there are a bunch of skinny branches all over the place. The drone was doing a pretty good job of following me along the road at a relatively slow speed, and then it clipped the branch that you can just barely see in the video above. It recovered in an acrobatic maneuver that has been mostly video-stabilized out, and resumed tracking me before I freaked and told it to land. You can see another example here, where the drone (again) clips a branch that has the sun behind it, and this clip shows me stopping my bike before the drone runs into another branch in a similar orientation. As the video shows, it’s very hard to see the branches until it’s too late.
As far as I can tell, the drone is no worse for wear from any of this, apart from a small nick in one of the props. But, this is a good illustration of a problematic situation for the Skydio 2: flying into a low sun angle around small bare branches. Should I not have been flying the drone in this situation? It’s hard to say. These probably qualify as “thin branches,” although there was plenty of room along with middle of the road. There is an open question with the Skydio 2 as to exactly how much responsibility the user should have about when and where it’s safe to fly—for branches, how thin is too thin? How low can the sun be? What if the branches are only kinda thin and the sun is only kinda low, but it’s also a little windy? Better to be safe than sorry, of course, but there’s really no way for the user (or the drone) to know what it can’t handle until it can’t handle it.
Edge cases like these aside, the obstacle avoidance just works. Even if you’re not deliberately trying to fly into branches, it’s keeping a lookout for you all the time, which means that flying the drone goes from somewhat stressful to just pure fun. I can’t emphasize enough how amazing it is to be able to fly without worrying about running into things, and how great it feels to be able to hand the controller to someone who’s never flown a drone before and say, with complete confidence, “go ahead, fly it around!”
Skydio 2 vs. DJI Mavic
Photo: Evan Ackerman/IEEE Spectrum
Both the Skydio 2 and many models of DJI’s Mavic use visual obstacle avoidance, but the Skydio 2 is so much more advanced that you can’t really compare the two systems.
It’s important to note that there’s a huge difference between the sort of obstacle avoidance that you get with a DJI Mavic, and the sort of obstacle avoidance that you get with the Skydio 2. The objective of the Mavic’s obstacle avoidance is really there to prevent you from accidentally running into things, and in that capacity, it usually works. But there are two things to keep in mind here—first, not running into things is not the same as avoiding things, because avoiding things means planning several steps ahead, not just one step.
Second, there’s the fact that the Mavic’s obstacle detection only works most of the time. Fundamentally, I don’t trust my Mavic Pro, because sometimes the safety system doesn’t kick in for whatever reason and the drone ends up alarmingly close to something. And that’s actually fine, because with the Mavic, I expect to be piloting it. It’s for this same reason that I don’t care that my Parrot Anafi doesn’t have obstacle avoidance at all: I’m piloting it anyway, and I’m a careful pilot, so it just doesn’t matter. The Skydio 2 is totally and completely different. It’s in a class by itself, and you can’t compare what it can do to what anything else out there right now. Period.
Skydio 2 Tracking
Skydio’s big selling point on the Skydio 2 is that it’ll autonomously track you while avoiding obstacles. It does this visually, by watching where you go, predicting your future motion, and then planning its own motion to keep you in frame. The works better than you might expect, in that it’s really very good at not losing you. Obviously, the drone prioritizes not running into stuff over tracking you, which means that it may not always be where you feel like it should be. It’s probably trying to get there, but in obstacle dense environments, it can take some creative paths.
Having said that, I found it to be very consistent with keeping me in the frame, and I only managed to lose it when changing direction while fully occluded by an obstacle, or while it was executing an avoidance maneuver that was more dynamic than normal. If you deliberately try to hide from the drone it’s not that hard to do so if there are enough obstacles around, but I didn’t find the tracking to be something that I had to worry about it most cases. When tracking does fail and you’re not using the beacon, the drone will come to a hover. It won’t try and find you, but it will reacquire you if you get back into its field of view.
The Skydio 2 had no problem tracking me running through fairly dense trees:
GIF: Evan Ackerman/IEEE Spectrum
The Skydio 2 had no problem chasing me around through these trees, even while I was asking it to continually change its tracking angle.
Click here for a full resolution clip.
It also managed to keep up with me as I rode my bike along a tree-lined road:
GIF: Evan Ackerman/IEEE Spectrum
The Skydio 2 is easily fast enough to keep up with me on a bike, even while avoiding tree branches.
Click here for a full resolution clip.
It lost me when I asked it to follow very close behind me as I wove through some particularly branch-y trees, but it fails more or less gracefully by just sort of nope-ing out of situations when they start to get bad and coming to a hover somewhere safe.
GIF: Evan Ackerman/IEEE Spectrum
The Skydio 2 knows better than to put itself into situations that it can’t handle, and will bail to a safe spot if things get too complicated.
Click here for a full resolution clip.
After a few days of playing with the drone, I started to get to the point where I could set it to track me and then just forget about it while I rode my bike or whatever, as opposed to constantly turning around to make sure it was still behind me, which is what I was doing initially. It’s a level of trust that I don’t think would be possible with any other drone.
Should You Buy a Skydio 2?
Photo: Evan Ackerman/IEEE Spectrum
We think the Skydio 2 is fun and relaxing to fly, with unique autonomous intelligence that makes it worth the cost.
In case I haven’t said it often enough in this review, the Skydio 2 is an incredible piece of technology. As far as I know (as a robotics journalist, mind you), this represents the state of the art in commercial drone autonomy, and quite possibly the state of the art in drone autonomy, period. And it’s available for $999, which is expensive, but less money than a Mavic Pro 2. If you’re interested in a new drone, you should absolutely consider the Skydio 2.
There are some things to keep in mind—battery life is a solid but not stellar 20 minutes. Extra batteries are expensive at $99 each (the base kit includes just one). The controller and the beacon are also expensive, at $150 each. And while I think the Skydio 2 is definitely the drone you want to fly, it may not be the drone you want to travel with, since it’s bulky compared to other options.
But there’s no denying the fact that the experience is uniquely magical. Once you’ve flown the Skydio 2, you won’t want to fly anything else. This drone makes it possible to get pictures and videos that would be otherwise impossible, and you can do it completely on your own. You can trust the drone to do what it promises, as long as you’re mindful of some basic and common sense safety guidelines. And we’ve been told that the drone is only going to get smarter and more capable over time.
If you buy a Skydio 2, it comes with the following warranty from Skydio:
“If you’re operating your Skydio 2 within our Safe Flight guidelines, and it crashes, we’ll repair or replace it for free.”
Skydio trusts their drone to go out into a chaotic and unstructured world and dodge just about anything that comes its way. And after a week with this drone, I can see how they’re able to offer this kind of guarantee. This is the kind of autonomy that robots have been promising for years, and the Skydio 2 makes it real.
Detailed technical specifications are available on Skydio’s website, and if you have any questions, post a comment—we’ve got this drone for a little while longer, and I’d be happy to try out (nearly) anything with it.
Skydio 2 Review Video Highlights
This video is about 7 minutes of 4K, 30 fps footage directly from the Skydio 2. The only editing I did was cutting clips together, no stabilization or color correcting or anything like that. The drone will record in 4K 60 fps, so it gets smoother than this, but I, er, forgot to change the setting.
[ Skydio ] Continue reading →
Posted in Human Robots
#436215 Help Rescuers Find Missing Persons With ...
There’s a definite sense that robots are destined to become a critical part of search and rescue missions and disaster relief efforts, working alongside humans to help first responders move faster and more efficiently. And we’ve seen all kinds of studies that include the claim “this robot could potentially help with disaster relief,” to varying degrees of plausibility.
But it takes a long time, and a lot of extra effort, for academic research to actually become anything useful—especially for first responders, where there isn’t a lot of financial incentive for further development.
It turns out that if you actually ask first responders what they most need for disaster relief, they’re not necessarily interested in the latest and greatest robotic platform or other futuristic technology. They’re using commercial off-the-shelf drones, often consumer-grade ones, because they’re simple and cheap and great at surveying large areas. The challenge is doing something useful with all of the imagery that these drones collect. Computer vision algorithms could help with that, as long as those algorithms are readily accessible and nearly effortless to use.
The IEEE Robotics and Automation Society and the Center for Robotic-Assisted Search and Rescue (CRASAR) at Texas A&M University have launched a contest to bridge this gap between the kinds of tools that roboticists and computer vision researchers might call “basic” and a system that’s useful to first responders in the field. It’s a simple and straightforward idea, and somewhat surprising that no one had thought of it before now. And if you can develop such a system, it’s worth some cash.
CRASAR does already have a Computer Vision Emergency Response Toolkit (created right after Hurricane Harvey), which includes a few pixel filters and some edge and corner detectors. Through this contest, you can get paid your share of a $3,000 prize pool for adding some other excessively basic tools, including:
Image enhancement through histogram equalization, which can be applied to electro-optical (visible light cameras) and thermal imagery
Color segmentation for a range
Grayscale segmentation for a range in a thermal image
If it seems like this contest is really not that hard, that’s because it isn’t. “The first thing to understand about this contest is that strictly speaking, it’s really not that hard,” says Robin Murphy, director of CRASAR. “This contest isn’t necessarily about coming up with algorithms that are brand new, or even state-of-the-art, but rather algorithms that are functional and reliable and implemented in a way that’s immediately [usable] by inexperienced users in the field.”
Murphy readily admits that some of what needs to be done is not particularly challenging at all, but that’s not the point—the point is to make these functionalities accessible to folks who have better things to do than solve these problems themselves, as Murphy explains.
“A lot of my research is driven by problems that I’ve seen in the field that you’d think somebody would have solved, but apparently not. More than half of this is available in OpenCV, but who’s going to find it, download it, learn Python, that kind of thing? We need to get these tools into an open framework. We’re happy if you take libraries that already exist (just don’t steal code)—not everything needs to be rewritten from scratch. Just use what’s already there. Some of it may seem too simple, because it IS that simple. It already exists and you just need to move some code around.”
If you want to get very slightly more complicated, there’s a second category that involves a little bit of math:
Coders must provide a system that does the following for each nadir image in a set:
Reads the geotag embedded in the .jpg
Overlays a USNG grid for a user-specified interval (e.g., every 50, 100, or 200 meters)
Gives the GPS coordinates of each pixel if a cursor is rolled over the image
Given a set of images with the GPS or USNG coordinate and a bounding box, finds all images in the set that have a pixel intersecting that location
The final category awards prizes to anyone who comes up with anything else that turns out to be useful. Or, more specifically, “entrants can submit any algorithm they believe will be of value.” Whether or not it’s actually of value will be up to a panel of judges that includes both first responders and computer vision experts. More detailed rules can be found here, along with sample datasets that you can use for testing.
The contest deadline is 16 December, so you’ve got about a month to submit an entry. Winners will be announced at the beginning of January. Continue reading →
Posted in Human Robots