Centipedes are known for their wiggly walk. With tens to hundreds of legs, they can traverse any terrain without stopping.

“When you see a scurrying centipede, you're basically seeing an animal that inhabits a world that is very different than our world of movement,” said Daniel Goldman, the Dunn Family Professor in the School of Physics. “Our movement is largely dominated by inertia. If I swing my leg, I land on my foot and I move forward. But in the world of centipedes, if they stop wiggling their body parts and limbs, they basically stop moving instantly.”

Intrigued to see if the many limbs could be helpful for locomotion in this world, a team of physicists, engineers, and mathematicians at the Georgia Institute of Technology are using this style of movement to their advantage. They developed a new theory of multilegged locomotion and created many-legged robotic models, discovering the robot with redundant legs could move across uneven surfaces without any additional sensing or control technology as the theory predicted.

These robots can move over complex, bumpy terrain — and there is potential to use them for agriculture, space exploration, and even search and rescue.

The researchers presented their work in the papers, “Multilegged Matter Transport: A Framework for Locomotion on Noisy Landscapes,” in Science in May and “Self-Propulsion via Slipping: Frictional Swimming in Multilegged Locomotors,” in Proceedings of the National Academy of Sciences in March.

A Leg Up

For the Science paper, the researchers were motivated by mathematician Claude Shannon’s communication theory, which demonstrates how to reliably transmit signals over distance, to understand why a multilegged robot was so successful at locomotion. The theory of communication suggests that one way to ensure a message gets from point A to point B on a noisy line isn’t to send it as an analog signal, but to break it into discrete digital units and repeat these units with an appropriate code.

“We were inspired by this theory, and we tried to see if redundancy could be helpful in matter transportation,” said Baxi Chong, a physics postdoctoral researcher. “So, we started this project to see what would happen if we had more legs on the robot: four, six, eight legs, and even 16 legs.”

A team led by Chong, including School of Mathematics postdoctoral fellow Daniel Irvine and Professor Greg Blekherman, developed a theory that proposes that adding leg pairs to the robot increases its ability to move robustly over challenging surfaces — a concept they call spatial redundancy. This redundancy makes the robot’s legs successful on their own without the need for sensors to interpret the environment. If one leg falters, the abundance of legs keeps it moving regardless. In effect, the robot becomes a reliable system to transport itself and even a load from A to B on difficult or “noisy” landscapes. The concept is comparable to how punctuality can be guaranteed on wheeled transport if the track or rail is smooth enough but without having to engineer the environment to create this punctuality.

“With an advanced bipedal robot, many sensors are typically required to control it in real time,” Chong said. “But in applications such as search and rescue, exploring Mars, or even micro robots, there is a need to drive a robot with limited sensing. There are many reasons for such sensor-free initiative. The sensors can be expensive and fragile, or the environments can change so fast that it doesn’t allow enough sensor-controller response time.”

To test this, Juntao He, a Ph.D. student in robotics, conducted a series of experiments where he and Daniel Soto, a master’s graduate in the George W. Woodruff School of Mechanical Engineering, built terrains to mimic an inconsistent natural environment. He then tested the robot by increasing its number of legs by two each time, starting with six and eventually expanding to 16. As the leg count increased, the robot could more agilely move across the terrain, even without sensors, as the theory predicted. Eventually, they tested the robot outdoors on real terrain, where it was able to traverse in a variety of environments.

“It's truly impressive to witness the multilegged robot's proficiency in navigating both lab-based terrains and outdoor environments,” Juntao said. “While bipedal and quadrupedal robots heavily rely on sensors to traverse complex terrain, our multilegged robot utilizes leg redundancy and can accomplish similar tasks with open-loop control.”

Next Steps

The researchers are already applying their discoveries to farming. Goldman has co-founded a company that aspires to use these robots to weed farmland where weedkillers are ineffective.

“They’re kind of like a Roomba but outside for complex ground,” Goldman said. “A Roomba works because it has wheels that function well on flat ground. Until the development of our framework, we couldn’t confidently predict locomotor reliability on bumpy, rocky, debris-ridden terrain. We now have the beginnings of such a scheme, which could be used to ensure that our robots traverse a crop field in a certain amount of time.”

The researchers also want to refine the robot. They know why the centipede robot framework is functional, but now they’re determining the optimal number of legs to achieve motion without sensing in a way that is cost-effective yet still retains the benefits.

“In this paper, we asked, ‘How do you predict the minimum number of legs to achieve such tasks?’” Chong said. “Currently we only prove that the minimum number exists, but we don't know that exact number of legs needed. Further, we need to better understand the tradeoff between energy, speed, power, and robustness in such a complex system.”

CITATION:

Baxi Chong et al., Multilegged matter transport: A framework for locomotion on noisy landscapes.Science380,509-515(2023).DOI:10.1126/science.ade4985

On April 26, 2023, the School of Physics and College of Sciences at Georgia Tech will welcome Stanford University physicist Steven Chu to speak on climate change and innovative paths towards a more sustainable future. Chu is the 1997 co-recipient of the Nobel Prize in Physics, and in his former role as U.S. Secretary of Energy, became the first scientist to hold a U.S. Cabinet position.

About the Talk

The event is part of the School of Physics “Inquiring Minds” public lecture series, and will be held at the Ferst Center for the Arts. The talk is free and open to campus and the Atlanta community, and no RSVP is required. Refreshments begin at 4:30, and the lecture will start at 5 p.m. ET.

“The multiple industrial and agricultural revolutions have transformed the world,” Chu recently shared in an abstract for the lecture. “However, an unintended consequence of this progress is that we are changing the climate of our planet. In addition to the climate risks, we will need to provide enough clean energy, water, and food for a more prosperous world that may grow to 11 billion by 2100.” 

The talk will discuss the significant technical challenges and potential solutions that could provide better paths to a more sustainable future. “How we transition from where we are now to where we need to be within 50 years is arguably the most pressing set of issues that science, innovation, and public policy have to address,” Chu added. 

The event’s faculty host is Daniel Goldman, Dunn Family Professor in the School of Physics at Georgia Tech.

About Steven Chu

Steven Chu is the William R. Kenan, Jr. Professor of Physics and a professor of Molecular and Cellular Physiology in the Medical School at Stanford University.

Chu served as the 12th U.S. Secretary of Energy from January 2009 until the end of April 2013. As the first scientist to hold a U.S. Cabinet position and the longest serving Energy Secretary, Chu led several initiatives including ARPA-E (Advanced Research Projects Agency – Energy), the Energy Innovation Hubs, and was personally tasked by President Obama to assist in the Deepwater Horizon oil leak.

In the spring of 2010, Chu was the keynote speaker for the Georgia Tech Ph.D. and Master's Commencement Ceremony.

Prior to his cabinet post, Chu was director of the Lawrence Berkeley National Laboratory, where he was active in pursuit of alternative and renewable energy technologies, and a professor of Physics and Applied Physics at Stanford, where he helped launch Bio-X, a multi-disciplinary institute combining the physical and biological sciences with medicine and engineering. Previously he also served as head of the Quantum Electronics Research Department at AT&T Bell Laboratories.

He is the co-recipient of the 1997 Nobel Prize in Physics for his contributions to laser cooling and atom trapping. He is a member of the National Academy of Sciences, the American Philosophical Society, the American Academy of Arts and Sciences, the Pontifical Academy Sciences, and of seven foreign academies. He formerly served as president, and then chair of the American Association for the Advancement of Science.

Chu earned an A.B. degree in mathematics and a B.S. degree in physics from the University of Rochester, and a Ph.D. in physics from the University of California, Berkeley, as well as 35 honorary degrees.

He has published over 280 papers in atomic and polymer physics, biophysics, biology, bio-imaging, batteries, and other energy technologies. He holds 15 patents, and an additional 15 patent disclosures or filings since 2015.

 

Five Georgia Tech College of Sciences researchers have been awarded CAREER grants from the National Science Foundation (NSF).

These Faculty Early Career Development Awards are part of a five-year funding mechanism designed to help promising researchers establish a personal foundation for a lifetime of leadership in their field. The grants are NSF’s most prestigious funding for untenured assistant professors.

Read more:

• Making Medicines: Vinayak Agarwal’s research into peptides, and their medicinal potential
• The Fundamental Questions: Jesse McDaniel’s new framework for predicting chemical reaction rates, leveraging computer modeling
• Chasing Chaos: Alex Blumenthal’s research in chaos, fluid dynamics
• Solving Infinite Problems: Anton Bernshteyn’s new, unified theory of descriptive combinatorics and distributed algorithms
• Gauging Glaciers: Alex Robel's new ice sheet modeling tool 

One of the most exciting parts of the CAREER grants is that they support new faculty, who are often working at the frontier of their fields. “I am excited about the CAREER research because we are really focusing on fundamental questions that are central to all of chemistry,” says Jesse McDaniel (School of Chemistry and Biochemistry) about his project, which focuses on creating a new framework to predict the rates of chemical reactions, leveraging computer science.

Anton Bernshteyn’s (School of Mathematics) work in the recently emerged field of descriptive combinatorics is also on the cutting edge of discovery. “There’s this new communication between separate fields of math and computer science— this huge synergy right now— it’s incredibly exciting,” Bernshteyn explains. “Right now we’re only starting to glimpse what’s possible.”

Each award also includes a teaching and outreach component: Vinayak Agarwal (School of Chemistry and Biochemistry) plans to use his grant to not only investigate peptides, but also to train the next generation of leaders, emphasizing student inclusion from diverse backgrounds: “The training is broadly applicable,” says Agarwal. “It will prepare students to move forward in STEM – and especially graduate studies – but will also prepare them for industry careers, government and regulatory science, graduate studies, and more. This kind of background is applicable in all fields.”

Alex Blumenthal (School of Mathematics), who is investigating the intersection of chaos, turbulence– including fluid dynamics– mathematics, and computer-assisted proof, agrees. “There’s a whole lot of new stuff to do,” Blumenthal says. “There’s a growing community of people studying random dynamics, and a growing community of people doing computer proofs– it’s a great place for undergrads to have meaningful research experiences.”

Alex Robel (School of Earth and Atmospheric Sciences), emphasizes the broad impacts of the CAREER grant projects. Robel is working to create a new ice sheet modeling tool, which will be accessible to anyone, and just require the use of a computer browser. “Ultimately,” Robel says, “this project will empower more people in the community to use these models and to use these models together with the observations that they're taking.”

Our world is powered by chemical reactions. From new medicines and biotechnology to sustainable energy solutions developing and understanding the chemical reactions behind innovations is a critical first step in pioneering new advances. And a key part of developing new chemistries is discovering how the rates of those chemical reactions can be accelerated or changed. 

For example, even an everyday chemical reaction, like toasting bread, can substantially change in speed and outcome — by increasing the heat, the speed of the reaction increases, toasting the bread faster. Adding another chemical ingredient — like buttering the bread before frying it — also changes the outcome of the reaction: the bread might brown and crisp rather than toast. The lesson? Certain chemical reactions can be accelerated or changed by adding or altering key variables, and understanding those factors is crucial when trying to create the desired reaction (like avoiding burnt toast!).

Chemists currently use quantum chemistry techniques to predict the rates and energies of chemical reactions, but the method is limited: predictions can usually only be made for up to a few hundred atoms. In order to scale the predictions to larger systems, and predict the environmental effects of reactions, a new framework needs to be developed.

Jesse McDaniel (School of Chemistry and Biochemistry) is creating that framework by leveraging computer modeling techniques. Now, a new NSF CAREER grant will help him do so. The National Science Foundation Faculty Early Career Development Award is a five-year grant designed to help promising researchers establish a foundation for a lifetime of leadership in their field. Known as CAREER awards, the grants are NSF’s most prestigious funding for untenured assistant professors. 

“I am excited about the CAREER research because we are really focusing on fundamental questions that are central to all of chemistry,” McDaniel says about the project.

Pioneering a new framework

“Chemical reactions are inherently quantum mechanical in nature,” McDaniel explains. “Electrons rearrange as chemical bonds are broken and formed.” While this type of quantum chemistry can allow scientists to predict the rates and energies of different reactions, these predictions are limited to only tens or hundreds of atoms. That’s where McDaniel’s team comes in. They’re developing modeling techniques based on quantum chemistry that could function over multiple scales, using computer models to scale the predictions. They hope this will help predict environmental effects on chemical reaction rates.

By developing modeling techniques that can be applied to reactions at multiple scales, McDaniel aims to expand scientist’s ability to predict and model chemical reactions, and how they interact with their environments. “Our goal is to understand the microscopic mechanisms and intermolecular interactions through which chemical reactions are accelerated within unique solvation environments such as microdroplets, thin films, and heterogenous interfaces,” McDaniel says. He hopes that it will allow for computational modeling of chemical reactions in much larger systems.  

Interdisciplinary research

As a theoretical and computational chemist, McDaniel’s chemistry experiments don’t take place in a typical chemistry lab — rather, they take place in a computer lab,  where Georgia Tech’s robust computer science and software development community functions as a key resource.

“We run computer simulations on high performance computing clusters,” McDaniel explains. “In this regard, we benefit from the HPC infrastructure at Georgia Tech, including the Partnership for an Advanced Computing Environment (PACE) team, as well as the computational resources provided in the new CODA building.” 

“Software is also a critical part of our research,” he continues. “My colleague Professor David Sherrill and his group are lead developers of the Psi4 quantum chemistry software, and this software comprises a core component of our multi-scale modeling efforts.”

In this respect, McDaniel is eager to to involve the next generation of chemists and computer scientists, showcasing the connection between these different fields. McDaniel’s team will partner with regional high school teachers, collaborating to integrate software and data science tools within the high school educational curriculum.

“One thing I like about this project,” McDaniel says, “is that all types of chemists — organic, inorganic, analytical, bio, physical, etc. — care about how chemical reactions happen, and how reactions are influenced by their surroundings.”

As climate change leads to rising sea levels and more powerful storms, coastal communities increasingly are turning to networks of sensors to track water levels. The sensors — which are progressively getting cheaper and more capable — can help officials anticipate flood risks and respond in emergencies.

A tool developed by Georgia Tech researchers can help make the most of those networks, pinpointing the ideal locations for water level sensors to maximize the real-time data available to emergency managers.

In a test case in Chatham County, Georgia, the approach developed by civil engineer Iris Tien reduced 29,000 potential sensor locations to just 381. The idea, then, is that officials can use their local expertise and historical knowledge to pick where to install sensors among those spots.

Read the full story on the College of Engineering website.

This press release is shared jointly with the Institute for Advanced Study (IAS) and NSF’s NOIRLab. It first appeared in the IAS newsroom.

A team of researchers, including astrophysicists from Georgia Tech, the Institute for Advanced Study, and NSF’s NOIRLab, has developed a new machine-learning technique to enhance the fidelity and sharpness of radio interferometric images. To demonstrate the power of their new approach, which is called PRIMO, the team created a new, high-fidelity version of the iconic Event Horizon Telescope's image of the supermassive black hole at the center of Messier 87, a giant elliptical galaxy located 55 million light-years from Earth.

The iconic image of the supermassive black hole at the center of M87—sometimes referred to as the “fuzzy, orange donut”—has gotten its first official makeover with the help of machine learning. The new image further exposes a central region that is larger and darker, surrounded by the bright accreting gas shaped like a “skinny donut.” The team used the data obtained by the Event Horizon Telescope (EHT) collaboration in 2017 and achieved, for the first time, the full resolution of the array.

“The new image of the M87 black hole showcases the remarkable power of the highest-resolution telescope on Earth working in tandem with modern machine learning algorithms; it demonstrates how technology continues to push the boundaries of our understanding of the universe,” said Feryal Özel, professor and chair of the School of Physics at Georgia Tech.

In 2017, the EHT collaboration used a network of seven pre-existing telescopes around the world to gather data on M87, creating an “Earth-sized telescope.” However, since it is infeasible to cover the Earth’s entire surface with telescopes, gaps arise in the data—like missing pieces in a jigsaw puzzle.

“With our new machine learning technique, PRIMO, we were able to achieve the maximum resolution of the current array,” says lead author Lia Medeiros of the Institute for Advanced Study. “Since we cannot study black holes up-close, the detail of an image plays a critical role in our ability to understand its behavior. The width of the ring in the image is now smaller by about a factor of two, which will be a powerful constraint for our theoretical models and tests of gravity.”

PRIMO, which stands for principal-component interferometric modeling, was developed by EHT members Lia Medeiros (Institute for Advanced Study), Dimitrios Psaltis (Georgia Tech), Tod Lauer (NOIRLab), and Feryal Özel (Georgia Tech). Their publication, “The Image of the M87 Black Hole Reconstructed with PRIMO,” is now available in The Astrophysical Journal Letters.

“PRIMO is a new approach to the difficult task of constructing images from EHT observations,” said Lauer. “It provides a way to compensate for the missing information about the object being observed, which is required to generate the image that would have been seen using a single gigantic radio telescope the size of the Earth.”

PRIMO relies on dictionary learning, a branch of machine learning which enables computers to generate rules based on large sets of training material. For example, if a computer is fed a series of different banana images—with sufficient training—it may be able to determine if an unknown image is or is not a banana. Beyond this simple case, the versatility of machine learning has been demonstrated in numerous ways: from creating Renaissance-style works of art to completing the unfinished work of Beethoven. So how might machines help scientists to render a black hole image? The research team has answered this very question.

With PRIMO, computers analyzed over 30,000 high-fidelity simulated images of black holes accreting gas. The ensemble of simulations covered a wide range of models for how the black hole accretes matter, looking for common patterns in the structure of the images. The various patterns of structure were sorted by how commonly they occured in the simulations, and were then blended to provide a highly accurate representation of the EHT observations, simultaneously providing a high fidelity estimate of the missing structure of the images. A paper pertaining to the algorithm itself was published in The Astrophysical Journal on February 3, 2023.

“We are using physics to fill in regions of missing data in a way that has never been done before by using machine learning,” added Medeiros. “This could have important implications for interferometry, which plays a role in fields from exo-planets to medicine.”

The team confirmed that the newly rendered image is consistent with the EHT data and with theoretical expectations, including the bright ring of emission expected to be produced by hot gas falling into the black hole. Generating an image required assuming an appropriate form of the missing information, and PRIMO did this by building on the 2019 discovery that the M87 black hole in broad detail looked as predicted.

“Approximately four years after the first horizon-scale image of a black hole was unveiled by EHT in 2019, we have marked another milestone, producing an image that utilizes the full resolution of the array for the first time,” stated Psaltis. “The new machine learning techniques that we have developed provide a golden opportunity for our collective work to understand black hole physics.”

The new image should lead to more accurate determinations of the mass of the M87 black hole and the physical parameters that determine its present appearance. The data also provides an opportunity for researchers to place greater constraints on alternatives to the event horizon (based on the darker central brightness depression) and perform more robust tests of gravity (based on the narrower ring size). PRIMO can also be applied to additional EHT observations, including those of Sgr A*, the central black hole in our own Milky Way galaxy.

M87 is a massive, relatively nearby, galaxy in the Virgo cluster of galaxies. Over a century ago, a mysterious jet of hot plasma was observed to emanate from its center. Beginning in the 1950s, the then new technique of radio astronomy showed the galaxy to have a compact bright radio source at its center. During the 1960s, M87 had been suspected to have a massive black hole at its center powering this activity. Measurements made from ground-based telescopes starting in the 1970s, and later the Hubble Space Telescope starting in the 1990s, provided strong support that M87 indeed harbored a black hole weighing several billion times the mass of the Sun based on observations of the high velocities of stars and gas orbiting its center. The 2017 EHT observations of M87 were obtained over several days from several different radio telescopes linked together at the same time to obtain the highest possible resolution. The now iconic “orange donut” picture of the M87 black hole, released in 2019, reflected the first attempt to produce an image from these observations.

“The 2019 image was just the beginning,” stated Medeiros. “If a picture is worth a thousand words, the data underlying that image have many more stories to tell. PRIMO will continue to be a critical tool in extracting such insights.”

Development of the PRIMO algorithm was enabled through the support of the National Science Foundation Astronomy and Astrophysics Postdoctoral Fellowship.

 

 

About Georgia Institute of Technology

The Georgia Institute of Technology, or Georgia Tech, is one of the top public research universities in the U.S., with more than 45,000 undergraduate and graduate students who study in person at the main campus in Atlanta, at Georgia Tech-Europe in France, at Georgia Tech-Shenzhen in China, as well as through distance and online learning. Students represent 50 states and more than 148 countries.

Georgia Tech's engineering and computing Colleges are the largest and among the highest-ranked in the nation, and the Institute also offers outstanding programs in business, design, liberal arts, and sciences.

With more than $1 billion annually in research awards across all six Colleges and the Georgia Tech Research Institute (GTRI), Georgia Tech is among the nation’s most research-intensive universities. It is an engine of economic development for the state of Georgia, the Southeast, and the nation.

Georgia Tech’s mission is to develop leaders who advance technology and improve the human condition. Its mission and strategic plan are focused on making a positive impact in the lives of people everywhere. Since 1885, the people of Georgia Tech have dared to imagine and then create solutions for a better future. The innovative culture and leadership continue — Progress and Service for all.

About the Institute for Advanced Study

The Institute for Advanced Study has served the world as one of the leading independent centers for theoretical research and intellectual inquiry since its establishment in 1930, advancing the frontiers of knowledge across the sciences and humanities. From the work of founding IAS faculty such as Albert Einstein and John von Neumann to that of the foremost thinkers of the present, the IAS is dedicated to enabling curiosity-driven exploration and fundamental discovery.

Each year, the Institute welcomes more than 200 of the world’s most promising post-doctoral researchers and scholars who are selected and mentored by a permanent Faculty, each of whom are preeminent leaders in their fields. Among present and past Faculty and Members there have been 35 Nobel Laureates, 44 of the 62 Fields Medalists, and 23 of the 26 Abel Prize Laureates, as well as many MacArthur Fellows and Wolf Prize winners.

 

Idling at a crossroads no longer, the automotive industry is embracing electrification like never before. With more electric vehicles purchased in 2022 than any year prior, consumers are beginning to follow their lead. Yet, while opportunity abounds, new challenges will require an innovative approach to ensure a sustainable and accessible electric future for all.

With historic investments from major players in the EV space, including Rivian, Kia, and Hyundai, the state of Georgia is uniquely positioned to serve as a leader in this effort. As the state's leading research institute, Georgia Tech is on the cutting edge of the movement. 

The transportation sector is the largest greenhouse gas emitter in the U.S. at nearly 30%, with passenger vehicles accounting for around 80% of the sector's total output1 as of 2019. Electric vehicles are widely regarded as a budding solution to reduce emissions, but even as both demand and production continue to increase, EVs currently account for around 1% of the cars on America's roadways. 

From the supply chain to the infrastructure needed to support alternative-fuel vehicles alongside consumer hesitancy, achieving the goals set by both the public and private sectors — including the Biden Administration's target of EVs making up at least 50% of new car sales by 2030 — will not be easy. Through research and development, policy, and collaboration, Tech experts are working toward finding solutions that will serve as catalysts during this transitionary period for the environment and the way Americans drive.

Check out the full story. 

This news release first appeared in the University of Arkansas Division of Agriculture newsroom, and has been tailored for Georgia Tech readers.

Researchers at Georgia Tech, the University of Arkansas System, the University of Nebraska-Lincoln, and Fort Valley State University in Georgia were awarded a $5 million grant to increase use of artificial intelligence and robotics in chicken processing to reduce waste in deboning and detect pathogens.

The grant from the U.S. Department of Agriculture’s National Institute of Food and Agriculture will establish the Center for Scalable and Intelligent Automation in Poultry Processing. The center, led by the University of Arkansas System Division of Agriculture, will join researchers from five institutions in three states in efforts to adapt robotic automation to chicken meat processing.

Douglas Britton, manager of the Agricultural Technology Research Program at the Georgia Tech Research Institute (GTRI), said his team was very excited to work on this project with experts at the four other institutions.

“The ultimate goal is to drive transformational innovation into the poultry and meat processing industry through automation, robotics, AI, and VR technologies,” Britton said. “Building on years of work in the GTRI Agricultural Technology Research Program, we are pleased to see that the USDA-NIFA has chosen this team to continue these efforts.”

Georgia Tech is a major partner in the project, and was awarded $2 million to focus on automating the processing lines that turn chickens into meat, said Jeyam Subbiah, professor and head of the food science department for the Division of Agriculture and the Dale Bumpers College of Agricultural, Food and Life Sciences at the University of Arkansas, and director of the project. The grant is for four years.

The Arkansas Agricultural Experiment Station, the research arm of the Division of Agriculture, will receive $2.2 million from the grant primarily to focus on food safety automation for poultry processing plants.

The remaining grant money will be divided between Julia McQuillan, Willa Cather professor of sociology at the University of Nebraska-Lincoln, and Brou Kuoakou, associate dean for research at Fort Valley State University in Georgia.

Jeff Buhr, a USDA Agricultural Research Service scientist, will contribute his expertise in broiler physiology to guide robotic deboning of meat, Subbiah said.

Georgia is the nation’s top broiler producer. Arkansas is number 3, according to 2021 figures from USDA.

Meeting the challenge

The recent impetus to automate chicken processing began with the Covid-19 pandemic, Subbiah said. The illness spread quickly among workers on the processing line. Since the worst of the pandemic, the poultry industry, like many others, has been having trouble hiring enough workers.

“Poultry processing lines began 70 to 80 years ago,” Subbiah said. “Since then, there have been only incremental changes in technology. Today, there’s a need for transformative change.”

Humans can feel when a knife hits a bone. In contrast, existing automation in poultry processing, like deboners, wastes a lot of meat.

“Human deboners leave about 13 percent of meat on the bones,” Subbiah said. “Automated deboners leave 16 to 17 percent. On an industrial scale, that’s a significant loss in value. We will use artificial intelligence and virtual reality to improve precision and reduce wastage.”

Automation can relieve labor shortages, Subbiah said. It also allows plants to locate in rural areas with a smaller labor force but nearer poultry houses and with lower property costs.

Initially, people working remotely may help advance robotic processing. Subbiah envisions workers logging on from home with virtual-reality goggles and haptics gloves to control robots located miles away.

While working remotely, the labor force will teach artificial intelligence how to cut up chickens of varying sizes and shapes.

“Automated machines right now are programmed to debone or cut up chickens based on an average size and shape. But no chicken is that size or shape,” Subbiah said. “Robot-wielded knives cut meat poorly. The machines have to learn how to adjust to the reality of random sizes and shapes.”

Georgia Tech’s participating scientists are all part of GTRI:

• Douglas Britton, manager of the Agricultural Technology Research Program
• Colin Trevor Usher, senior research scientist and branch head of robotics systems and technology, Agricultural Technology Research Program
• Ai-Ping Hu, principal research engineer, Agricultural Technology Research Program
• Konrad Ahlin, research engineer, Intelligent Sustainable Technologies Division
• Michael Park, research engineer, Intelligent Sustainable Technologies Division
• Benjamin Joffe, research scientist, Intelligent Sustainable Technologies Division
• Shreyes Melkote, the Morris M. Bryan, Jr. Professorship in Mechanical Engineering, associate director of the Georgia Tech Manufacturing Institute and executive director of the Novelis Innovation Hub

“We are thrilled to partner with our colleagues here in the Division of Agriculture, as well as our colleagues at Georgia Tech and the other participating institutions on this exciting project,” said David Caldwell, head of the Division of Agriculture’s poultry science department and director of the Center of Excellence for Poultry Science.

“We expect the findings from these coordinated research projects will be impactful for our stakeholders in the commercial poultry industry here in Northwest Arkansas and throughout the entire industry,” Caldwell said. “This project will help keep moving technology forward in processing and food safety of poultry.”

For more information about the project, see the original press release on the University of Arkansas Division of Agriculture website.

Through its interdisciplinary research, service-based learning, and innovative coursework, Georgia Tech’s School of Civil and Environmental Engineering is a leader in systems-level thinking and technological innovation at the interface of built, natural, information, and social systems. The school aims to not only define the challenges and complex problems facing humanity and the environment, but to catalyze the solutions to solve them.

This installment of the Faces of Research Q&A series is with Joe F. Bozeman III, assistant professor in the School of Civil and Environmental Engineering, the School of Public Policy, and director of the Social Equity and Environmental Engineering Lab (SEEEL).

What is your field of expertise and why did you choose it?
I research and develop equitable climate change adaptation and mitigation strategies anchored in environmental engineering practice. My current focus areas are urbanization, food-energy-water, and circularity (e.g., circular materials and the circular economy). I chose this path because I felt that I could merge my lived experiences, having come from humble beginnings, with the technical aspects of engineering and public policy to realize more equitable infrastructure and policy outcomes.  

What makes Georgia Tech research institutes unique?
Georgia Tech’s research institutes have an existing system which allows for collaboration across scientific disciplines and with real community members. This is something that I think is uniquely beneficial for folks like me. That is, for my research to have real-world impact, I need access to faculty and community collaborators who share an equity-centered mindset. 

What impact is your research having on the world?
It has been wonderful to see my research enter broader community and academic spaces through mainstream media, scientific publications, regulatory deliberation, and even art. For instance, my work on U.S. food-consumption impacts — for example, greenhouse gas emissions, land, and water impact that come from what we eat — across sociodemographic subgroups (Black, Latinx, white, and socioeconomic status) was featured in a range of media outlets including NPR, the New York Post, Popular Science, Free Speech TV, and political radio programs. Other aspects of my research have established international research priorities for cities, or urban systems, and even inform some of the music you may have heard on network TV and streaming services. My lab, the Social Equity and Environmental Engineering Lab (SEEEL), is exploring other ways to merge equity, engineering, and art in meaningful ways.     

What is the most challenging aspect of your research?
For SEEEL activities, acquiring and fairly distributing money, and time resources is the most challenging part. The concept of integrating systemic equity into existing engineering practices is new. This is exciting in many ways. However, it also presents challenges when it comes to developing standards around flexible funding access, community-based research and development, and establishing criteria to evaluate how well systemic equity is being achieved in various domains (e.g., within research labs, within governmental bodies, and for actual community members). Through these types of efforts, I hope to play a role in regaining some of the public’s trust in academia.

If you weren't a researcher, what would you be?
If I weren’t a researcher, I probably would have continued as a music sound engineer, producer, and performer. As I previously mentioned, I hope to leverage my experience in the arts to help translate some of the technical engineering findings into content that all of us can easily digest (e.g., songs, video, film, and physical art). I’d even go as far as to say that I think there is room to make the technical engineering findings, in their original form, more accessible to the broader public. This has compelled SEEEL to master the art of effective writing and presentation delivery.

What was the first thing you remember wanting to be when you were a kid?
As a kid, I first wanted to be a NBA player. Ironically, I listed becoming an engineer as a very close second. Back then, I believe I thought of engineering as a means to video game and sound design.

This news release first appeared in the Chinese Academy of Sciences newsroom, and has been tailored for Georgia Tech readers.

Mycorrhizal symbiosis — a symbiotic relationship that can exist between fungi and plant roots — helps plants expand their root surface area, giving plants greater access to nutrients and water. Although the first and foremost role of mycorrhizal symbiosis is to facilitate plant nutrition, scientists have not been clear how mycorrhizal types mediate the nutrient acquisition and interactions of coexisting trees in forests.  

To investigate this crucial relationship, Lingli Liu, a professor at the Institute of Botany of the Chinese Academy of Sciences (IBCAS) led an international, collaborative team, which included School of Biological Sciencesprofessor Lin Jiang. The team studied nutrient acquisition strategies of arbuscular mycorrhizae (AM) and ectomycorrhizal (EcM) trees in the Biodiversity–Ecosystem Functioning (BEF) experiment in a subtropical forest in China, where trees of the two mycorrhizal types were initially evenly planted in mixtures of two, four, eight, or 16 tree species.   

The researchers found that as the diversity of species increased, the net primary production (NPP) of EcM trees rapidly decreased, but the NPP of AM trees progressively increased, leading to the sheer dominance (>90%) of AM trees in the highest diversity treatment. 

The team's analyses further revealed that differences in mycorrhizal nutrient-acquisition strategies, both nutrient acquisition from soil and nutrient resorption within the plant, contribute to the competitive edge of AM trees over EcM ones.  

In addition, analysis of soil microbial communities showed that EcM-tree monocultures have a high abundance of symbiotic fungi, whereas AM-tree monocultures were dominated by saprotrophic and pathogenic fungi.  

According to the researchers, as tree richness increased, shifts in microbial communities, particularly a decrease in the relative abundance of Agaricomycetes (mainly EcM fungi), corresponded with a decrease in the NPP of EcM subcommunities, but had a relatively small impact on the NPP of AM subcommunities.  

These findings suggest that more efficient nutrient-acquisition strategies, rather than microbial-mediated negative plant-soil feedback, drive the dominance of AM trees in high-diversity ecosystems.  

This study, based on the world’s largest forest BEF experiment, provides novel data and an alternative mechanism for explaining why and how AM trees usually dominate in high-diversity subtropical forests.

These findings also have practical implications for species selection in tropical and subtropical reforestation—suggesting it is preferable to plant mixed AM trees, as they have a more efficient nutrient-acquisition strategy than EcM trees.  

This study was published as an online cover article in Sciences Advances on Jan. 19 and was funded by the Strategic Priority Research Program of CAS and the National Natural Science Foundation of China.