Tag Archives: SEA

#438014 Meet Blueswarm, a Smart School of ...

Posted on January 29, 2021 by Android

Anyone who’s seen an undersea nature documentary has marveled at the complex choreography that schooling fish display, a darting, synchronized ballet with a cast of thousands.

Those instinctive movements have inspired researchers at the Harvard John A. Paulson School of Engineering and Applied Sciences (SEAS), and the Wyss Institute for Biologically Inspired Engineering. The results could improve the performance and dependability of not just underwater robots, but other vehicles that require decentralized locomotion and organization, such as self-driving cars and robotic space exploration.

The fish collective called Blueswarm was created by a team led by Radhika Nagpal, whose lab is a pioneer in self-organizing systems. The oddly adorable robots can sync their movements like biological fish, taking cues from their plastic-bodied neighbors with no external controls required. Nagpal told IEEE Spectrum that this marks a milestone, demonstrating complex 3D behaviors with implicit coordination in underwater robots.

“Insights from this research will help us develop future miniature underwater swarms that can perform environmental monitoring and search in visually-rich but fragile environments like coral reefs,” Nagpal said. “This research also paves a way to better understand fish schools, by synthetically recreating their behavior.”

The research is published in Science Robotics, with Florian Berlinger as first author. Berlinger said the “Bluedot” robots integrate a trio of blue LED lights, a lithium-polymer battery, a pair of cameras, a Raspberry Pi computer and four controllable fins within a 3D-printed hull. The fish-lens cameras detect LED’s of their fellow swimmers, and apply a custom algorithm to calculate distance, direction and heading.

Based on that simple production and detection of LED light, the team proved that Blueswarm could self-organize behaviors, including aggregation, dispersal and circle formation—basically, swimming in a clockwise synchronization. Researchers also simulated a successful search mission, an autonomous Finding Nemo. Using their dispersion algorithm, the robot school spread out until one could detect a red light in the tank. Its blue LEDs then flashed, triggering the aggregation algorithm to gather the school around it. Such a robot swarm might prove valuable in search-and-rescue missions at sea, covering miles of open water and reporting back to its mates.

“Each Bluebot implicitly reacts to its neighbors’ positions,” Berlinger said. The fish—RoboCod, perhaps?—also integrate a Wifi module to allow uploading new behaviors remotely. The lab’s previous efforts include a 1,000-strong army of “Kilobots,” and a robotic construction crew inspired by termites. Both projects operated in two-dimensional space. But a 3D environment like air or water posed a tougher challenge for sensing and movement.

In nature, Berlinger notes, there’s no scaly CEO to direct the school’s movements. Nor do fish communicate their intentions. Instead, so-called “implicit coordination” guides the school’s collective behavior, with individual members executing high-speed moves based on what they see their neighbors doing. That decentralized, autonomous organization has long fascinated scientists, including in robotics.

“In these situations, it really benefits you to have a highly autonomous robot swarm that is self-sufficient. By using implicit rules and 3D visual perception, we were able to create a system with a high degree of autonomy and flexibility underwater where things like GPS and WiFi are not accessible.”

Berlinger adds the research could one day translate to anything that requires decentralized robots, from self-driving cars and Amazon warehouse vehicles to exploration of faraway planets, where poor latency makes it impossible to transmit commands quickly. Today’s semi-autonomous cars face their own technical hurdles in reliably sensing and responding to their complex environments, including when foul weather obscures onboard sensors or road markers, or when they can’t fix position via GPS. An entire subset of autonomous-car research involves vehicle-to-vehicle (V2V) communications that could give cars a hive mind to guide individual or collective decisions— avoiding snarled traffic, driving safely in tight convoys, or taking group evasive action during a crash that’s beyond their sensory range.

“Once we have millions of cars on the road, there can’t be one computer orchestrating all the traffic, making decisions that work for all the cars,” Berlinger said.

The miniature robots could also work long hours in places that are inaccessible to humans and divers, or even large tethered robots. Nagpal said the synthetic swimmers could monitor and collect data on reefs or underwater infrastructure 24/7, and work into tiny places without disturbing fragile equipment or ecosystems.

“If we could be as good as fish in that environment, we could collect information and be non-invasive, in cluttered environments where everything is an obstacle,” Nagpal said. Continue reading →

Posted in Human Robots

#437978 How Mirroring the Architecture of the ...

Posted on January 18, 2021 by Android

While AI can carry out some impressive feats when trained on millions of data points, the human brain can often learn from a tiny number of examples. New research shows that borrowing architectural principles from the brain can help AI get closer to our visual prowess.

The prevailing wisdom in deep learning research is that the more data you throw at an algorithm, the better it will learn. And in the era of Big Data, that’s easier than ever, particularly for the large data-centric tech companies carrying out a lot of the cutting-edge AI research.

Today’s largest deep learning models, like OpenAI’s GPT-3 and Google’s BERT, are trained on billions of data points, and even more modest models require large amounts of data. Collecting these datasets and investing the computational resources to crunch through them is a major bottleneck, particularly for less well-resourced academic labs.

It also means today’s AI is far less flexible than natural intelligence. While a human only needs to see a handful of examples of an animal, a tool, or some other category of object to be able pick it out again, most AI need to be trained on many examples of an object in order to be able to recognize it.

There is an active sub-discipline of AI research aimed at what is known as “one-shot” or “few-shot” learning, where algorithms are designed to be able to learn from very few examples. But these approaches are still largely experimental, and they can’t come close to matching the fastest learner we know—the human brain.

This prompted a pair of neuroscientists to see if they could design an AI that could learn from few data points by borrowing principles from how we think the brain solves this problem. In a paper in Frontiers in Computational Neuroscience, they explained that the approach significantly boosts AI’s ability to learn new visual concepts from few examples.

“Our model provides a biologically plausible way for artificial neural networks to learn new visual concepts from a small number of examples,” Maximilian Riesenhuber, from Georgetown University Medical Center, said in a press release. “We can get computers to learn much better from few examples by leveraging prior learning in a way that we think mirrors what the brain is doing.”

Several decades of neuroscience research suggest that the brain’s ability to learn so quickly depends on its ability to use prior knowledge to understand new concepts based on little data. When it comes to visual understanding, this can rely on similarities of shape, structure, or color, but the brain can also leverage abstract visual concepts thought to be encoded in a brain region called the anterior temporal lobe (ATL).

“It is like saying that a platypus looks a bit like a duck, a beaver, and a sea otter,” said paper co-author Joshua Rule, from the University of California Berkeley.

The researchers decided to try and recreate this capability by using similar high-level concepts learned by an AI to help it quickly learn previously unseen categories of images.

Deep learning algorithms work by getting layers of artificial neurons to learn increasingly complex features of an image or other data type, which are then used to categorize new data. For instance, early layers will look for simple features like edges, while later ones might look for more complex ones like noses, faces, or even more high-level characteristics.

First they trained the AI on 2.5 million images across 2,000 different categories from the popular ImageNet dataset. They then extracted features from various layers of the network, including the very last layer before the output layer. They refer to these as “conceptual features” because they are the highest-level features learned, and most similar to the abstract concepts that might be encoded in the ATL.

They then used these different sets of features to train the AI to learn new concepts based on 2, 4, 8, 16, 32, 64, and 128 examples. They found that the AI that used the conceptual features yielded much better performance than ones trained using lower-level features on lower numbers of examples, but the gap shrunk as they were fed more training examples.

While the researchers admit the challenge they set their AI was relatively simple and only covers one aspect of the complex process of visual reasoning, they said that using a biologically plausible approach to solving the few-shot problem opens up promising new avenues in both neuroscience and AI.

“Our findings not only suggest techniques that could help computers learn more quickly and efficiently, they can also lead to improved neuroscience experiments aimed at understanding how people learn so quickly, which is not yet well understood,” Riesenhuber said.

As the researchers note, the human visual system is still the gold standard when it comes to understanding the world around us. Borrowing from its design principles might turn out to be a profitable direction for future research.

Image Credit: Gerd Altmann from Pixabay Continue reading →

Posted in Human Robots

#437697 These Underwater Drones Use Water ...

Posted on November 25, 2020 by Android

Yi Chao likes to describe himself as an “armchair oceanographer” because he got incredibly seasick the one time he spent a week aboard a ship. So it’s maybe not surprising that the former NASA scientist has a vision for promoting remote study of the ocean on a grand scale by enabling underwater drones to recharge on the go using his company’s energy-harvesting technology.

Many of the robotic gliders and floating sensor stations currently monitoring the world’s oceans are effectively treated as disposable devices because the research community has a limited number of both ships and funding to retrieve drones after they’ve accomplished their mission of beaming data back home. That’s not only a waste of money, but may also contribute to a growing assortment of abandoned lithium-ion batteries polluting the ocean with their leaking toxic materials—a decidedly unsustainable approach to studying the secrets of the underwater world.

“Our goal is to deploy our energy harvesting system to use renewable energy to power those robots,” says Chao, president and CEO of the startup Seatrec. “We're going to save one battery at a time, so hopefully we're going to not to dispose more toxic batteries in the ocean.”

Chao’s California-based startup claims that its SL1 Thermal Energy Harvesting System can already help save researchers money equivalent to an order of magnitude reduction in the cost of using robotic probes for oceanographic data collection. The startup is working on adapting its system to work with autonomous underwater gliders. And it has partnered with defense giant Northrop Grumman to develop an underwater recharging station for oceangoing drones that incorporates Northrop Grumman’s self-insulating electrical connector capable of operating while the powered electrical contacts are submerged.

Seatrec’s energy-harvesting system works by taking advantage of how certain substances transition from solid-to-liquid phase and liquid-to-gas phase when they heat up. The company’s technology harnesses the pressure changes that result from such phase changes in order to generate electricity.

Image: Seatrec

To make the phase changes happen, Seatrec’s solution taps the temperature differences between warmer water at the ocean surface and colder water at the ocean depths. Even a relatively simple robotic probe can generate additional electricity by changing its buoyancy to either float at the surface or sink down into the colder depths.

By attaching an external energy-harvesting module, Seatrec has already begun transforming robotic probes into assets that can be recharged and reused more affordably than sending out a ship each time to retrieve the probes. This renewable energy approach could keep such drones going almost indefinitely barring electrical or mechanical failures. “We just attach the backpack to the robots, we give them a cable providing power, and they go into the ocean,” Chao explains.

The early buyers of Seatrec’s products are primarily academic researchers who use underwater drones to collect oceanographic data. But the startup has also attracted military and government interest. It has already received small business innovation research contracts from both the U.S. Office of Naval Research and National Oceanic and Atmospheric Administration (NOAA).

Seatrec has also won two $10,000 prizes under the Powering the Blue Economy: Ocean Observing Prize administered by the U.S. Department of Energy and NOAA. The prizes awarded during the DISCOVER Competition phase back in March 2020 included one prize split with Northrop Grumman for the joint Mission Unlimited UUV Station concept. The startup and defense giant are currently looking for a robotics company to partner with for the DEVELOP Competition phase of the Ocean Observing Prize that will offer a total of $3 million in prizes.

In the long run, Seatrec hopes its energy-harvesting technology can support commercial ventures such as the aquaculture industry that operates vast underwater farms. The technology could also support underwater drones carrying out seabed surveys that pave the way for deep sea mining ventures, although those are not without controversy because of their projected environmental impacts.

Among all the possible applications Chao seems especially enthusiastic about the prospect of Seatrec’s renewable power technology enabling underwater drones and floaters to collect oceanographic data for much longer periods of time. He spent the better part of two decades working at the NASA Jet Propulsion Laboratory in Pasadena, Calif., where he helped develop a satellite designed for monitoring the Earth’s oceans. But he and the JPL engineering team that developed Seatrec’s core technology believe that swarms of underwater drones can provide a continuous monitoring network to truly begin understanding the oceans in depth.

The COVID-19 pandemic has slowed production and delivery of Seatrec’s products somewhat given local shutdowns and supply chain disruptions. Still, the startup has been able to continue operating in part because it’s considered to be a defense contractor that is operating an essential manufacturing facility. Seatrec’s engineers and other staff members are working in shifts to practice social distancing.

“Rather than building one or two for the government, we want to scale up to build thousands, hundreds of thousands, hopefully millions, so we can improve our understanding and provide that data to the community,” Chao says. Continue reading →

Posted in Human Robots

#437416 Robotics firm expands autonomous data ...

Posted on September 24, 2020 by Android

Back in 2013, local Brooklyn papers were excitedly reporting on a new initiative aimed at getting residents involved in cleaning up the highly polluted Gowanus Canal. Brooklyn Atlantis, as the project was known, was the brainchild of NYU Tandon Professor of Mechanical and Aerospace Engineering Maurizio Porfiri, who envisioned building and launching robotic boats to collect water-quality data and capture images of the infamous canal, which citizen scientists would then view and help classify. Those robotic boats ultimately led to the formation of the company Manifold Robotics, which aimed to further develop the unmanned surface vehicles (USVs) with sensor technology. (The fledgling company received support from PowerBridgeNY, a collaborative initiative to bring university research to market.) More recently, the startup has now branched out to develop a mobile data collection platform that allows unmanned aerial vehicles (UAVs) to operate safely in the sky near power lines. Continue reading →

Posted in Human Robots

#437395 Microsoft Had a Crazy Idea to Put ...

Posted on September 18, 2020 by Android

A little over two years ago, a shipping container-sized cylinder bearing Microsoft’s name and logo was lowered onto the ocean floor off the northern coast of Scotland. Inside were 864 servers, and their submersion was part of the second phase of the software giant’s Project Natick. Launched in 2015, the project’s purpose is to determine the feasibility of underwater data centers powered by offshore renewable energy.

A couple months ago, the deep-sea servers were brought back up to the surface so engineers could inspect them and evaluate how they’d performed while under water.

But wait—why were they there in the first place?

As bizarre as it seems to sink hundreds of servers into the ocean, there are actually several very good reasons to do so. According to the UN, about 40 percent of the world’s population lives within 60 miles of an ocean. As internet connectivity expands to cover most of the globe in the next few years, millions more people will come online, and a lot more servers will be needed to manage the increased demand and data they’ll generate.

In densely-populated cities real estate is expensive and can be hard to find. But know where there’s lots of cheap, empty space? At the bottom of the ocean. This locale also carries the added benefit of being really cold (depending where we’re talking, that is; if you’re looking off the coast of, say, Mumbai or Abu Dhabi, the waters are warmer).

Servers generate a lot of heat, and datacenters use most of their electricity for cooling. Keeping not just the temperature but also the humidity level constant is important for optimal functioning of the servers; neither of these vary much 100 feet under water.

Finally, installing data centers on the ocean floor is, surprisingly, much faster than building them on land. Microsoft claims its server-holding cylinders will take less than 90 days to go from factory ship to operation, as compared to the average two years it takes to get a terrestrial data center up and running.

Microsoft’s Special Projects team operated the underwater data center for two years, and it took a full day to dredge it up and bring it to the surface. One of the first things researchers did was to insert test tubes into the container to take samples of the air inside; they’ll use it to try to determine how gases released from the equipment may have impacted the servers’ operating environment.

The container was filled with dry nitrogen upon deployment, which seems to have made for a much better environment than the oxygen that land-bound servers are normally surrounded by; the failure rate of the servers in the water was just one-eighth that of Microsoft’s typical rate for its servers on land. The team thinks the nitrogen atmosphere was helpful because it’s less corrosive than oxygen. The fact that no humans entered the container for the entirety of its operations helped, too (no moving around of components or having to turn on lights or adjust the temperature).

Ben Cutler, a project manager in Microsoft’s Special Projects research group who leads Project Natick, believes the results of this phase of the project are sufficient to show that underwater data centers are worth pursuing. “We are now at the point of trying to harness what we have done as opposed to feeling the need to go and prove out some more,” he said.

Cutler envisions putting underwater datacenters near offshore wind farms to power them sustainably. The data centers of the future will require less human involvement, instead being managed and run primarily by technologies like robotics and AI. In this kind of “lights-out” datacenter, the servers would be swapped out about once every five years, with any that fail before then being taken offline.

The final step in this phase of Project Natick is to recycle all the components used for the underwater data center, including the steel pressure vessel, heat exchangers, and the servers themselves—and restoring the sea bed where the cylinder rested back to its original condition.

If Cutler’s optimism is a portent of things to come, it may not be long before the ocean floor is dotted with sustainable datacenters to feed our ever-increasing reliance on our phones and the internet.

Image Credit: Microsoft Continue reading →

Posted in Human Robots