Tag Archives: robot

#438006 Smellicopter Drone Uses Live Moth ...

Research into robotic sensing has, understandably I guess, been very human-centric. Most of us navigate and experience the world visually and in 3D, so robots tend to get covered with things like cameras and lidar. Touch is important to us, as is sound, so robots are getting pretty good with understanding tactile and auditory information, too. Smell, though? In most cases, smell doesn’t convey nearly as much information for us, so while it hasn’t exactly been ignored in robotics, it certainly isn’t the sensing modality of choice in most cases.

Part of the problem with smell sensing is that we just don’t have a good way of doing it, from a technical perspective. This has been a challenge for a long time, and it’s why we either bribe or trick animals like dogs, rats, vultures, and other animals to be our sensing systems for airborne chemicals. If only they’d do exactly what we wanted them to do all the time, this would be fine, but they don’t, so it’s not.

Until we get better at making chemical sensors, leveraging biology is the best we can do, and what would be ideal would be some sort of robot-animal hybrid cyborg thing. We’ve seen some attempts at remote controlled insects, but as it turns out, you can simplify things if you don’t use the entire insect, but instead just find a way to use its sensing system. Enter the Smellicopter.

There’s honestly not too much to say about the drone itself. It’s an open-source drone project called Crazyflie 2.0, with some additional off the shelf sensors for obstacle avoidance and stabilization. The interesting bits are a couple of passive fins that keep the drone pointed into the wind, and then the sensor, called an electroantennogram.

Image: UW

The drone’s sensor, called an electroantennogram, consists of a “single excised antenna” from a Manduca sexta hawkmoth and a custom signal processing circuit.

To make one of these sensors, you just, uh, “harvest” an antenna from a live hawkmoth. Obligingly, the moth antenna is hollow, meaning that you can stick electrodes up it. Whenever the olfactory neurons in the antenna (which is still technically alive even though it’s not attached to the moth anymore) encounter an odor that they’re looking for, they produce an electrical signal that the electrodes pick up. Plug the other ends of the electrodes into a voltage amplifier and filter, run it through an analog to digital converter, and you’ve got a chemical sensor that weighs just 1.5 gram and consumes only 2.7 mW of power. It’s significantly more sensitive than a conventional metal-oxide odor sensor, in a much smaller and more efficient form factor, making it ideal for drones.

To localize an odor, the Smellicopter uses a simple bioinspired approach called crosswind casting, which involves moving laterally left and right and then forward when an odor is detected. Here’s how it works:

The vehicle takes off to a height of 40 cm and then hovers for ten seconds to allow it time to orient upwind. The smellicopter starts casting left and right crosswind. When a volatile chemical is detected, the smellicopter will surge 25 cm upwind, and then resume casting. As long as the wind direction is fairly consistent, this strategy will bring the insect or robot increasingly closer to a singular source with each surge.

Since odors are airborne, they need a bit of a breeze to spread very far, and the Smellicopter won’t be able to detect them unless it’s downwind of the source. But, that’s just how odors work— even if you’re right next to the source, if the wind is blowing from you towards the source rather than the other way around, you might not catch a whiff of it.

Whenever the olfactory neurons in the antenna encounter an odor that they’re looking for, they produce an electrical signal that the electrodes pick up

There are a few other constraints to keep in mind with this sensor as well. First, rather than detecting something useful (like explosives), it’s going to detect the smells of pretty flowers, because moths like pretty flowers. Second, the antenna will literally go dead on you within a couple hours, since it only functions while its tissues are alive and metaphorically kicking. Interestingly, it may be possible to use CRISPR-based genetic modification to breed moths with antennae that do respond to useful smells, which would be a neat trick, and we asked the researchers—Melanie Anderson, a doctoral student of mechanical engineering at the University of Washington, in Seattle; Thomas Daniel, a UW professor of biology; and Sawyer Fuller, a UW assistant professor of mechanical engineering—about this, along with some other burning questions, via email.

IEEE Spectrum, asking the important questions first: So who came up with “Smellicopter”?

Melanie Anderson: Tom Daniel coined the term “Smellicopter”. Another runner up was “OdorRotor”!

In general, how much better are moths at odor localization than robots?

Melanie Anderson: Moths are excellent at odor detection and odor localization and need to be in order to find mates and food. Their antennae are much more sensitive and specialized than any portable man-made odor sensor. We can't ask the moths how exactly they search for odors so well, but being able to have the odor sensitivity of a moth on a flying platform is a big step in that direction.

Tom Daniel: Our best estimate is that they outperform robotic sensing by at least three orders of magnitude.

How does the localization behavior of the Smellicopter compare to that of a real moth?

Anderson: The cast-and-surge odor search strategy is a simplified version of what we believe the moth (and many other odor searching animals) are doing. It is a reactive strategy that relies on the knowledge that if you detect odor, you can assume that the source is somewhere up-wind of you. When you detect odor, you simply move upwind, and when you lose the odor signal you cast in a cross-wind direction until you regain the signal.

Can you elaborate on the potential for CRISPR to be able to engineer moths for the detection of specific chemicals?

Anderson: CRISPR is already currently being used to modify the odor detection pathways in moth species. It is one of our future efforts to specifically use this to make the antennae sensitive to other chemicals of interest, such as the chemical scent of explosives.

Sawyer Fuller: We think that one of the strengths of using a moth's antenna, in addition to its speed, is that it may provide a path to both high chemical specificity as well as high sensitivity. By expressing a preponderance of only one or a few chemosensors, we are anticipating that a moth antenna will give a strong response only to that chemical. There are several efforts underway in other research groups to make such specific, sensitive chemical detectors. Chemical sensing is an area where biology exceeds man-made systems in terms of efficiency, small size, and sensitivity. So that's why we think that the approach of trying to leverage biological machinery that already exists has some merit.

You mention that the antennae lifespan can be extended for a few days with ice- how feasible do you think this technology is outside of a research context?

Anderson: The antennae can be stored in tiny vials in a standard refrigerator or just with an ice pack to extend their life to about a week. Additionally, the process for attaching the antenna to the electrical circuit is a teachable skill. It is definitely feasible outside of a research context.

Considering the trajectory that sensor development is on, how long do you think that this biological sensor system will outperform conventional alternatives?

Anderson: It's hard to speak toward what will happen in the future, but currently, the moth antenna still stands out among any commercially-available portable sensors.

There have been some experiments with cybernetic insects; what are the advantages and disadvantages of your approach, as opposed to (say) putting some sort of tracking system on a live moth?

Daniel: I was part of a cyber insect team a number of years ago. The challenge of such research is that the animal has natural reactions to attempts to steer or control it.

Anderson: While moths are better at odor tracking than robots currently, the advantage of the drone platform is that we have control over it. We can tell it to constrain the search to a certain area, and return after it finishes searching.

What can you tell us about the health, happiness, and overall wellfare of the moths in your experiments?

Anderson: The moths are cold anesthetized before the antennae are removed. They are then frozen so that they can be used for teaching purposes or in other research efforts.

What are you working on next?

Daniel: The four big efforts are (1) CRISPR modification, (2) experiments aimed at improving the longevity of the antennal preparation, (3) improved measurements of antennal electrical responses to odors combined with machine learning to see if we can classify different odors, and (4) flight in outdoor environments.

Fuller: The moth's antenna sensor gives us a new ability to sense with a much shorter latency than was previously possible with similarly-sized sensors (e.g. semiconductor sensors). What exactly a robot agent should do to best take advantage of this is an open question. In particular, I think the speed may help it to zero in on plume sources in complex environments much more quickly. Think of places like indoor settings with flow down hallways that splits out at doorways, and in industrial settings festooned with pipes and equipment. We know that it is possible to search out and find odors in such scenarios, as anybody who has had to contend with an outbreak of fruit flies can attest. It is also known that these animals respond very quickly to sudden changes in odor that is present in such turbulent, patchy plumes. Since it is hard to reduce such plumes to a simple model, we think that machine learning may provide insights into how to best take advantage of the improved temporal plume information we now have available.

Tom Daniel also points out that the relative simplicity of this project (now that the UW researchers have it all figured out, that is) means that even high school students could potentially get involved in it, even if it’s on a ground robot rather than a drone. All the details are in the paper that was just published in Bioinspiration & Biomimetics. Continue reading

Posted in Human Robots

#437990 Video Friday: Record-Breaking Drone Show ...

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!):

HRI 2021 – March 8-11, 2021 – [Online]
RoboSoft 2021 – April 12-16, 2021 – [Online]
Let us know if you have suggestions for next week, and enjoy today's videos.

A new parent STAR robot is presented. The parent robot has a tail on which the child robot can climb. By collaborating together, the two robots can reach locations that neither can reach on its own.

The parent robot can also supply the child robot with energy by recharging its batteries. The parent STAR can dispatch and recuperate the child STAR automatically (when aligned). The robots are fitted with sensors and controllers and have automatic capabilities but make no decisions on their own.

[ Bio-Inspired and Medical Robotics Lab ]

How TRI trains its robots.

[ TRI ]

The only thing more satisfying than one SCARA robot is two SCARA robots working together.

[ Fanuc ]

I'm not sure that this is strictly robotics, but it's so cool that it's worth a watch anyway.

[ Shinoda & Makino Lab ]

Flying insects heavily rely on optical flow for visual navigation and flight control. Roboticists have endowed small flying robots with optical flow control as well, since it requires just a tiny vision sensor. However, when using optical flow, the robots run into two problems that insects appear to have overcome. Firstly, since optical flow only provides mixed information on distances and velocities, using it for control leads to oscillations when getting closer to obstacles. Secondly, since optical flow provides very little information on obstacles in the direction of motion, it is hardest to detect obstacles that the robot is actually going to collide with! We propose a solution to these problems by means of a learning process.

[ Nature ]

A new Guinness World Record was set on Friday in north China for the longest animation performed by 600 unmanned aerial vehicles (UAVs).

[ Xinhua ]

Translucency is prevalent in everyday scenes. As such, perception of transparent objects is essential for robots to perform manipulation. In this work, we propose LIT, a two-stage method for transparent object pose estimation using light-field sensing and photorealistic rendering.

[ University of Michigan ] via [ Fetch Robotics ]

This paper reports the technological progress and performance of team “CERBERUS” after participating in the Tunnel and Urban Circuits of the DARPA Subterranean Challenge.

And here's a video report on the SubT Urban Beta Course performance:

[ CERBERUS ]

Congrats to Energy Robotics on 2 million euros in seed funding!

[ Energy Robotics ]

Thanks Stefan!

In just 2 minutes, watch HEBI robotics spending 23 minutes assembling a robot arm.

HEBI Robotics is hosting a webinar called 'Redefining the Robotic Arm' next week, which you can check out at the link below.

[ HEBI Robotics ]

Thanks Hardik!

Achieving versatile robot locomotion requires motor skills which can adapt to previously unseen situations. We propose a Multi-Expert Learning Architecture (MELA) that learns to generate adaptive skills from a group of representative expert skills. During training, MELA is first initialised by a distinct set of pre-trained experts, each in a separate deep neural network (DNN). Then by learning the combination of these DNNs using a Gating Neural Network (GNN), MELA can acquire more specialised experts and transitional skills across various locomotion modes.

[ Paper ]

Since the dawn of history, advances in science and technology have pursued “power” and “accuracy.” Initially, “hardness” in machines and materials was sought for reliable operations. In our area of Science of Soft Robots, we have combined emerging academic fields aimed at “softness” to increase the exposure and collaboration of researchers in different fields.

[ Science of Soft Robots ]

A team from the Laboratory of Robotics and IoT for Smart Precision Agriculture and Forestry at INESC TEC – Technology and Science are creating a ROS stack solution using Husky UGV for precision field crop agriculture.

[ Clearpath Robotics ]

Associate Professor Christopher J. Hasson in the Department of Physical Therapy is the director Neuromotor Systems Laboratory at Northeastern University. There he is working with a robotic arm to provide enhanced assistance to physical therapy patients, while maintaining the intimate therapist and patient relationship.

[ Northeastern ]

Mobile Robotic telePresence (MRP) systems aim to support enhanced collaboration between remote and local members of a given setting. But MRP systems also put the remote user in positions where they frequently rely on the help of local partners. Getting or ‘recruiting’ such help can be done with various verbal and embodied actions ranging in explicitness. In this paper, we look at how such recruitment occurs in video data drawn from an experiment where pairs of participants (one local, one remote) performed a timed searching task.

[ Microsoft Research ]

A presentation [from Team COSTAR] for the American Geophysical Union annual fall meeting on the application of robotic multi-sensor 3D Mapping for scientific exploration of caves. Lidar-based 3D maps are combined with visual/thermal/spectral/gas sensors to provide rich 3D context for scientific measurements map.

[ COSTAR ] Continue reading

Posted in Human Robots

#437988 Bio-in­spired ro­bot­ics: Learn­ing ...

It is a high-speed movement: within fractions of a second the mouthparts of the dragonfly larvae spring forwards to seize its prey. For decades, researchers had assumed that this action must have been driven primarily by hydraulic pressure. Now, for the first time, scientists at Kiel University (CAU) have completely decrypted the biomechanical functional principle of what is known as the labial mask of dragonfly larvae. A vital contribution to this discovery was made by the team led by Dr. Sebastian Büsse of the Zoological Institute in its development of a bio-inspired robot with the operating principle of the complex mouthparts adapted to test its own hypothesis—the technology used here could lead to a significant enhancement of agile robot systems. The results of the ambitious research project were published on Wednesday 20 January in the renowned specialist journal Science Robotics. Continue reading

Posted in Human Robots

#437984 WSR: A new Wi-Fi-based system for ...

Researchers at Harvard University have recently devised a system based on Wi-Fi sensing that could enhance the collaboration between robots operating in unmapped environments. This system, presented in a paper pre-published on arXiv, can essentially emulate antenna arrays in the air as a robot moves freely in a 2-D or 3-D environment. Continue reading

Posted in Human Robots

#437982 Superintelligent AI May Be Impossible to ...

It may be theoretically impossible for humans to control a superintelligent AI, a new study finds. Worse still, the research also quashes any hope for detecting such an unstoppable AI when it’s on the verge of being created.

Slightly less grim is the timetable. By at least one estimate, many decades lie ahead before any such existential computational reckoning could be in the cards for humanity.

Alongside news of AI besting humans at games such as chess, Go and Jeopardy have come fears that superintelligent machines smarter than the best human minds might one day run amok. “The question about whether superintelligence could be controlled if created is quite old,” says study lead author Manuel Alfonseca, a computer scientist at the Autonomous University of Madrid. “It goes back at least to Asimov’s First Law of Robotics, in the 1940s.”

The Three Laws of Robotics, first introduced in Isaac Asimov's 1942 short story “Runaround,” are as follows:

A robot may not injure a human being or, through inaction, allow a human being to come to harm.
A robot must obey the orders given it by human beings except where such orders would conflict with the First Law.
A robot must protect its own existence as long as such protection does not conflict with the First or Second Laws.

In 2014, philosopher Nick Bostrom, director of the Future of Humanity Institute at the University of Oxford, not only explored ways in which a superintelligent AI could destroy us but also investigated potential control strategies for such a machine—and the reasons they might not work.

Bostrom outlined two possible types of solutions of this “control problem.” One is to control what the AI can do, such as keeping it from connecting to the Internet, and the other is to control what it wants to do, such as teaching it rules and values so it would act in the best interests of humanity. The problem with the former is that Bostrom thought a supersmart machine could probably break free from any bonds we could make. With the latter, he essentially feared that humans might not be smart enough to train a superintelligent AI.

Now Alfonseca and his colleagues suggest it may be impossible to control a superintelligent AI, due to fundamental limits inherent to computing itself. They detailed their findings this month in the Journal of Artificial Intelligence Research.

The researchers suggested that any algorithm that sought to ensure a superintelligent AI cannot harm people had to first simulate the machine’s behavior to predict the potential consequences of its actions. This containment algorithm then would need to halt the supersmart machine if it might indeed do harm.

However, the scientists said it was impossible for any containment algorithm to simulate the AI’s behavior and predict with absolute certainty whether its actions might lead to harm. The algorithm could fail to correctly simulate the AI’s behavior or accurately predict the consequences of the AI’s actions and not recognize such failures.

“Asimov’s first law of robotics has been proved to be incomputable,” Alfonseca says, “and therefore unfeasible.”

We may not even know if we have created a superintelligent machine, the researchers say. This is a consequence of Rice’s theorem, which essentially states that one cannot in general figure anything out about what a computer program might output just by looking at the program, Alfonseca explains.

On the other hand, there’s no need to spruce up the guest room for our future robot overlords quite yet. Three important caveats to the research still leave plenty of uncertainty to the group’s predictions.

First, Alfonseca estimates AI’s moment of truth remains, he says, “At least two centuries in the future.”

Second, he says researchers do not know if so-called artificial general intelligence, also known as strong AI, is theoretically even feasible. “That is, a machine as intelligent as we are in an ample variety of fields,” Alfonseca explains.

Last, Alfonseca says, “We have not proved that superintelligences can never be controlled—only that they can’t always be controlled.”

Although it may not be possible to control a superintelligent artificial general intelligence, it should be possible to control a superintelligent narrow AI—one specialized for certain functions instead of being capable of a broad range of tasks like humans. “We already have superintelligences of this type,” Alfonseca says. “For instance, we have machines that can compute mathematics much faster than we can. This is [narrow] superintelligence, isn’t it?” Continue reading

Posted in Human Robots