Bio-inspired underwater devices and robotic vehicle swarm algorithms


MIT self-propelled wave swimmers

Assistant professor Wim van Rees and his team have developed simulations of self-propelled undulating swimmers to better understand how fish-like deformable fins can improve the propulsion of underwater equipment, seen here in a top view. Credit: Image Courtesy of MIT of Rees Lab

OF ocean and mechanical engineers use advances in scientific computing to address the ocean’s many challenges and seize its opportunities.

Few environments are as unforgiving as the ocean. Unpredictable weather patterns and communication limitations have left large swaths of the ocean unexplored and shrouded in mystery.

“The ocean is a fascinating environment with a number of current challenges such as microplastics, algal blooms, coral bleaching and rising temperatures,” said Wim van Rees, the ABS Career Development Professor at MIT. “At the same time, the ocean offers countless opportunities — from aquaculture to energy harvesting and exploring the many ocean creatures we have yet to discover.”

Ocean engineers and mechanical engineers, such as Van Rees, use advances in scientific computing to address the ocean’s many challenges and seize its opportunities. These researchers are developing technologies to better understand our oceans, and how both organisms and human-made vehicles can move in them, from microscale to macroscale.

Self Propelled Wave Swimmers

Assistant professor Wim van Rees and his team have developed simulations of self-propelled undulating swimmers to better understand how fish-like deformable fins can improve the propulsion of underwater equipment, seen here as two fish side by side. Credit: Image Courtesy of MIT of Rees Lab

Bio-Inspired Underwater Devices

An intricate dance takes place as fish dart through the water. Flexible fins flap in water currents, leaving a trail of eddies.

“Fish have intricate internal muscles to adjust the precise shape of their bodies and fins. This allows them to propel themselves in many different ways, well beyond what a man-made vehicle can do in terms of maneuverability, maneuverability or adaptability,” explains Van Rees.

According to Van Rees, thanks to advances in additive manufacturing, optimization techniques and machine learning, we are closer than ever to replicating flexible and changing fish fins for use in underwater robotics. As such, there is a greater need to understand how these soft fins affect propulsion.

Van Rees and his team are developing and using numerical simulation approaches to explore the design space for underwater devices with an increase in degrees of freedom, for example due to fish-like, deformable fins.

Forecast of Loop Current Eddies

Graduate student Abhinav Gupta and professor Pierre Lermusiaux have developed a new machine learning framework to compensate for the lack of resolution or accuracy in existing dynamic system models. Their framework can be used for a number of applications, including improved predictions of tread current vortices around oil rigs in the Gulf of Mexico. Credit: Image Courtesy of the MIT MSEAS Lab

These simulations help the team better understand the interplay between the fluid and structural mechanics of fish’s soft, flexible fins as they move through a fluid stream. This allows them to better understand how fin shape deformations can harm or improve swimming performance. “By developing precise numerical techniques and scalable parallel implementations, we can use supercomputers to solve exactly what happens at this interface between the flow and the structure,” adds Van Rees.

By combining his simulation algorithms for flexible underwater structures with optimization and machine learning techniques, Van Rees aims to develop an automated design tool for a new generation of autonomous underwater devices. This tool can help engineers and designers develop, for example, robotic fins and underwater vehicles that can intelligently adapt their shape to better achieve their immediate operational goals – be it swimming or maneuvering faster and more efficiently.

“We can use this optimization and AI to create inverse design across the parameter space and create smart, adaptable devices from scratch, or use precise individual simulations to identify the physical principles that determine why one shape outperforms another. explains Van Rees. .

Robotic vehicle swarm algorithms

Like Van Rees, lead researcher Michael Benjamin wants to improve the way vehicles maneuver through water. In 2006, then a postdoc at MIT, Benjamin launched an open-source software project for an autonomous steering technology he developed. The software, which has been used by companies such as Sea Machines, BAE/Riptide, Thales UK and Rolls Royce, as well as the United States Navy, uses a new method of multi-objective optimization. This optimization method, developed by Benjamin during his PhD research, allows a vehicle to autonomously choose the course, speed, depth and direction in which it should go in order to achieve multiple simultaneous objectives.

Swarm algorithms for unmanned vehicles

Michael Benjamin has developed swarming algorithms that allow unmanned vehicles, such as those depicted, to disperse in an optimal distribution and avoid collisions. Credit: Michael Benjamin

Benjamin is now taking this technology a step further by developing swarm and obstacle avoidance algorithms. With these algorithms, dozens of unmanned vehicles could communicate with each other and explore a certain part of the ocean.

To start, Benjamin looks at how best to distribute autonomous vehicles in the ocean.

“Suppose you want to launch 50 vehicles in a part of the Sea of ​​Japan. We want to know: does it make sense to drop all 50 vehicles in one place, or have a mothership drop them off at certain points in a certain area?” explains Benjamin.

He and his team have developed algorithms that answer this question. Using swarming technology, each vehicle periodically communicates its location with other vehicles nearby. Benjamin’s software allows these vehicles to spread out in an optimal distribution for the part of the ocean in which they operate.

Central to the success of the swarming vehicles is the ability to avoid collisions. Collision avoidance is complicated by international maritime regulations known as COLREGS – or ‘Collision Regulations’. These rules determine which vehicles have right of way when crossing paths, posing a unique challenge to Benjamin’s swarm algorithms.

The COLREGS were written from the perspective of avoiding another single contact, but Benjamin’s swarm algorithm had to account for multiple unmanned vehicles trying to avoid colliding with each other.

To address this problem, Benjamin and his team created a multi-object optimization algorithm that ranked specific maneuvers on a scale from zero to 100. A zero would be a direct collision, while 100 would mean the vehicles avoid a collision completely. .

“Our software is the only marine software where multi-objective optimization is the mathematical basis for decision making,” says Benjamin.

While researchers like Benjamin and van Rees are using machine learning and multi-objective optimization to address the complexity of vehicles moving through ocean environments, others like Pierre Lermusiaux, the Nam Pyo Suh Professor at MIT, are using machine learning to better understand the ocean environment itself. to understand.

Improving ocean modeling and forecasting

Oceans are perhaps the best example of what is known as a complex dynamic system. Fluid dynamics, changing tides, weather patterns and climate change make the ocean an unpredictable environment that changes from moment to moment. The ever-changing nature of the ocean environment can make predictions incredibly difficult.

Researchers have used dynamic systems models to make predictions for ocean environments, but as Lermusiaux explains, these models have their limitations.

“You can’t take every molecule of water in the ocean into account when developing models. The resolution and accuracy of models, and ocean measurements are limited. There can be a model data point every 100 meters, every kilometer, or, if you look at climate models of the global ocean, you can have a data point every 10 kilometers. That can have a major impact on the accuracy of your prediction,” explains Lermusiaux.

Graduate student Abhinav Gupta and Lermusiaux have developed a new machine learning framework to compensate for the lack of resolution or accuracy in these models. Their algorithm takes a simple, low-resolution model and can fill in the gaps, emulating a more accurate, more complex, high-resolution model.

For the first time, the Gupta and Lermusiaux framework learns time delays in existing estimated models to improve their predictive capabilities.

“Things in the natural world don’t happen instantly; however, all mainstream models assume that things happen in real time,” says Gupta. “To make an approximate model more accurate, the machine learning and data you input into the equation must reflect the effects of past states on the future prediction.”

The team’s “neural closure model,” which explains these delays, could potentially lead to improved predictions for things like a loop current hitting an oil rig in the Gulf of Mexico, or the amount of phytoplankton in a particular area of ​​the ocean.

As computer technologies such as Gupta and Lermusiaux’s neural closure model continue to improve and advance, researchers can unravel more of the ocean’s mysteries and develop solutions to the many challenges our oceans face.

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