SlothBot is a slow-moving and energy-efficient robot that can linger in the trees to monitor animals, plants, and the environment below, researchers report.

For the next several months, visitors to the Atlanta Botanical Garden will be able to observe the testing of the new high-tech tool in the battle to save some of the world’s most endangered species. Researchers are testing the SlothBot near the Garden’s popular Canopy Walk.

Built to take advantage of the low-energy lifestyle of real sloths, SlothBot demonstrates how being slow can be ideal for certain applications. Powered by solar panels and using innovative power management technology, SlothBot moves along a cable strung between two large trees as it monitors temperature, weather, carbon dioxide levels, and other information in the Garden’s 30-acre midtown Atlanta forest.

“SlothBot embraces slowness as a design principle,” says Magnus Egerstedt, professor and chair in the Georgia Institute of Technology’s School of Electrical and Computer Engineering.

“That’s not how robots are typically designed today, but being slow and hyper-energy efficient will allow SlothBot to linger in the environment to observe things we can only see by being present continuously for months, or even years.”

About three feet long, SlothBot’s whimsical 3D-printed shell helps protect its motors, gearing, batteries, and sensing equipment from the weather. The robot is programmed to move only when necessary, and will locate sunlight when its batteries need recharging.

At the Atlanta Botanical Garden, SlothBot will operate on a single 100-foot cable, but in larger environmental applications, it will be able to switch from cable to cable to cover more territory.

“The most exciting goal we’ll demonstrate with SlothBot is the union of robotics and technology with conservation,” says Emily Coffey, vice president for conservation and research at the Garden. “We do conservation research on imperiled plants and ecosystems around the world, and SlothBot will help us find new and exciting ways to advance our research and conservation goals.”

SlothBot could help scientists better understand the abiotic factors affecting critical ecosystems, providing a new tool for developing information needed to protect rare species and endangered ecosystems.

“SlothBot could do some of our research remotely and help us understand what’s happening with pollinators, interactions between plants and animals, and other phenomena that are difficult to observe otherwise,” Coffey adds.

“With the rapid loss of biodiversity and with more than a quarter of the world’s plants potentially heading toward extinction, SlothBot offers us another way to work toward conserving those species.”

Inspiration for the robot came from a visit Egerstedt made to a vineyard in Costa Rica where he saw two-toed sloths creeping along overhead wires in their search for food in the tree canopy.

“It turns out that they were strategically slow, which is what we need if we want to deploy robots for long periods of time,” he says.

A few other robotic systems have already demonstrated the value of slowness. Among the best known are the Mars Exploration Rovers that gathered information on the red planet for more than a dozen years.

“Speed wasn’t really all that important to the Mars Rovers,” Egerstedt notes. “But they learned a lot during their leisurely exploration of the planet.”

Beyond conservation, SlothBot could have applications for precision agriculture, where the robot’s camera and other sensors traveling in overhead wires could provide early detection of crop diseases, measure humidity, and watch for insect infestation. After testing in the Atlanta Botanical Garden, the researchers hope to move SlothBot to South America to observe orchid pollination or the lives of endangered frogs.

The researchers considered multiple locomotion techniques for the SlothBot. Wheeled robots are common, but in the natural world they can easily be defeated by obstacles like rocks or mud. Flying robots require too much energy to linger for long. That’s why Egerstedt’s observation of the wire-crawling sloths was so important.

“It’s really fascinating to think about robots becoming part of the environment, a member of an ecosystem,” he says. “While we’re not building an anatomical replica of the living sloth, we believe our robot can be integrated to be part of the ecosystem it’s observing like a real sloth.”

Beyond their conservation goals, the researchers hope SlothBot will provide a new way to stimulate interest in conservation from the Garden’s visitors. “This will help us tell the story of the merger between technology and conservation,” Coffey says. “It’s a unique way to engage the public and bring forward a new way to tell our story.”

And that should be especially interesting to children visiting the Garden.

“This new way of thinking about robots should trigger curiosity among the kids who will walk by it,” says Egerstedt.

“Thanks to SlothBot, I’m hoping we will get an entirely new generation interested in what robotics can do to make the world better.”

The SlothBot launched in the Atlanta Botanical Garden is the second version of a system originally reported in May 2019 at the International Conference on Robotics and Automation.

Funding for the work came from the US Office of Naval Research and the National Science Foundation. The content is solely the responsibility of the authors and does not necessarily represent the official views of the sponsoring agencies.

Source: Georgia Tech

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The way roaches run could teach robots a thing or two

A cleverly simple method to assess and improve locomotion in robots gets its inspiration from the superb scurrying skills of cockroaches.

Normally, understanding how insects’ or robots’ moving parts coordinate smoothly to take them places requires tedious modeling of mechanics, electronics, and information science. But in a new study, biomechanics researchers boiled down the sprints of cockroaches to handy principles and equations they then used to make a test robot amble about better.

The method told the researchers about how each leg operates on its own, how they all come together as a whole, and the harmony or lack thereof in how they do it. Despite bugs’ and bots’ utterly divergent motion dynamics, the new method worked for both and should work for other robots and animals, too.

The biological robot, the roach, was the far superior runner with neurological signals guiding six impeccably evolved legs. The mechanical robot, a consumer model, had four stubby legs and no nervous system but relied instead for locomotion control on coarse physical forces traveling through its chassis as crude signals to roughly coordinate its clunky gait.

“The robot was much bulkier and could hardly sense its environment. The cockroach had many senses and can adapt better to rough terrain. Bumps as high as its hips wouldn’t slow it down at all,” says first author Izaak Neveln, who was a postdoctoral researcher in the lab of Simon Sponberg, an assistant professor in the School of Physics and in the School of Biological Sciences at the Georgia Institute of Technology, during the study.

A universal method

The method, or “measure,” as the study calls it, transcended these huge differences, which pervade animal-inspired robotics.

“The measure is general (universal) in the sense that it can be used regardless of whether the signals are neural spiking patterns, kinematics, voltages, or forces and does not depend on the particular relationship between the signals,” the study’s authors write.

No matter how a bug or a bot functions, the measure’s mathematical inputs and outputs are always in the same units. The measure will not always eliminate the need for modeling, but it stands to shorten and guide modeling and avert anguishing missteps.

Why stick bugs move so strangely

Often a bot or an animal sends many walking signals through a central system to harmonize locomotion, but not all signals are centralized. Even in humans, though locomotion strongly depends on signals from the central nervous system, some neural signals are confined to regions of the body; they are localized signals.

Some insects appear to move with little centralization—such as stick bugs, also known as walking sticks, whose legs prod about nearly independently. Stick bugs are wonky runners.

“The idea has been that the stick bugs have the more localized control of motion, whereas a cockroach goes very fast and needs to maintain stability, and its motion control is probably more centralized, more clocklike,” Neveln says.

Strong centralization of signals generally coordinates locomotion better. Centralized signals could be code traveling through an elaborate robot’s wiring, a cockroach’s central neurons synching its legs, or the clunky robot’s chassis tilting away from a leg thumping the ground and putting weight onto an opposing leg.

Roboticists need to see through the differences and figure out the interplay of a locomotor’s local and central signals.

Metronomes and cockroaches

The new “measure” does this by focusing on an overarching phenomenon in the walking legs, which can be seen as pendula moving back and forth. For great locomotion, they need to synch up in what is called phase-coupling oscillations.

A fun, easy experiment illustrates this physics principle. If a few, say six, metronomes—ticking rhythm pendula that piano teachers use—are swinging out of sync, and you place them all on a platform that freely sways along with the metronomes’ swings, the swings will sync up in unison.

The phases, or directions, of their oscillations are coupling with each other by centralizing their composite mechanical impulses through the platform. This particular example of phase-coupling is mechanical, but it can also be computational or neurological—like in the roach.

Its legs would be analogous to the swinging metronomes, and central neuromuscular activity analogous to the free-swaying platform. In the roach, not all six legs swing in the same direction.

“Their synchronization is not uniform. Three legs are synchronized in phase with each other—the front and back legs of one side with the middle leg of the other side—and those three are synchronized out of phase with the other three,” Neveln says. “It’s an alternating tripod gait. One tripod of three legs alternates with the other tripod of three legs.”

Better robot movement

And just like pendula, each leg’s swings can be graphed as a wave. All the legs’ waves can be averaged into an overall roach scurry wave and then developed into more useful math that relates centralization with decentralization and factors like entropy that can throw locomotion control off.

The resulting principles and math benefited the clunky robot, which has strong decentralized signals in its leg motors that react to leg contact with the ground, and centralized control weaker than that of the stick bug. The researchers graphed out the robot’s movements, too, but they didn’t result in the neatly synced group of waves that the cockroach’s produced.

The researchers turned with the principles and math to the clunky robot, which initially was out of sorts—bucking or hopping uselessly like a pogo stick. Then the scientists strengthened centralized control by reweighting its chassis to make it move more coherently.

“The metronomes on the platform are mechanical coupling, and our robot coordinates control that way,” Neveln says. “You can change the mechanical coupling of the robot by repositioning its weights. We were able to predict the changes this would make by using the measure we developed from the cockroach.”

The researchers also wired up specific roach muscles and neurons to observe their syncopations with the scurry waves. Seventeen cockroaches took 2,982 strides to inform the principles and math, and the bugs also sprung surprises on the researchers.

One stuck out: The scientists had thought signaling centralized more when the roach sped up, but instead, both central and local signaling strengthened, perhaps doubling down on the message to run.

The study appears in the journal Nature Communications. The National Science Foundation funded the research. Any findings, conclusions, and recommendations are those of the authors and not necessarily of the NSF.

Source: Georgia Tech

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