MIT students and researchers from MIT Sea Grant work with local oyster farmers in advancing the aquaculture industry by seeking solutions to some of its biggest challenges. Currently, oyster bags have to be manually flipped every one to two weeks to reduce biofouling. Image: John Freidah, MIT MechE
By Michaela Jarvis | Department of Mechanical Engineering
When Michelle Kornberg was about to graduate from MIT, she wanted to use her knowledge of mechanical and ocean engineering to make the world a better place. Luckily, she found the perfect senior capstone class project: supporting sustainable seafood by helping aquaculture farmers grow oysters.
“It’s our responsibility to use our skills and opportunities to work on problems that really matter,” says Kornberg, who now works for an aquaculture company called Innovasea. “Food sustainability is incredibly important from an environmental standpoint, of course, but it also matters on a social level. The most vulnerable will be hurt worst by the climate crisis, and I think food sustainability and availability really matters on that front.”
The project undertaken by Kornberg’s capstone class, 2.017 (Design of Electromechanical Robotic Systems), came out of conversations between Michael Triantafyllou, who is MIT’s Henry L. and Grace Doherty Professor in Ocean Science and Engineering and director of MIT Sea Grant, and Dan Ward. Ward, a seasoned oyster farmer and marine biologist, owns Ward Aquafarms on Cape Cod and has worked extensively to advance the aquaculture industry by seeking solutions to some of its biggest challenges.
Speaking with Triantafyllou at MIT Sea Grant — part of a network of university-based programs established by the federal government to protect the coastal environment and economy — Ward had explained that each of his thousands of floating mesh oyster bags need to be turned over about 11 times a year. The flipping allows algae, barnacles, and other “biofouling” organisms that grow on the part of the bag beneath the water’s surface to be exposed to air and light, so they can dry and chip off. If this task is not performed, water flow to the oysters, which is necessary for their growth, is blocked.
The bags are flipped by a farmworker in a kayak, and the task is monotonous, often performed in rough water and bad weather, and ergonomically injurious. “It’s kind of awful, generally speaking,” Ward says, adding that he pays about $3,500 per year to have the bags turned over at each of his two farm sites — and struggles to find workers who want to do the job of flipping bags that can grow to a weight of 60 or 70 pounds just before the oysters are harvested.
Presented with this problem, the capstone class Kornberg was in — composed of six students in mechanical engineering, ocean engineering, and electrical engineering and computer science — brainstormed solutions. Most of the solutions, Kornberg says, involved an autonomous robot that would take over the bag-flipping. It was during that class that the original version of the “Oystamaran,” a catamaran with a flipping mechanism between its two hulls, was born.
A combination of mechanical engineering, ocean engineering, and electrical engineering and computer sciences students work together to design a robot to help with flipping oyster bags at Ward Aquafarm on Cape Cod. The “Oystamaran” robot uses a vision system to position and flip the bags. Image: Lauren Futami, MIT MechE
Ward’s involvement in the project has been important to its evolution. He says he has reviewed many projects in his work on advisory boards that propose new technologies for aquaculture. Often, they don’t correspond with the actual challenges faced by the industry.
“It was always ‘I already have this remotely operated vehicle; would it be useful to you as an oyster farmer if I strapped on some kind of sensor?’” Ward says. “They try to fit robotics into aquaculture without any industry collaboration, which leads to a robotic product that doesn’t solve any of the issues we experience out on the farm. Having the opportunity to work with MIT Sea Grant to really start from the ground up has been exciting. Their approach has been, ‘What’s the problem, and what’s the best way to solve the problem?’ We do have a real need for robotics in aquaculture, but you have to come at it from the customer-first, not the technology-first, perspective.”
Triantafyllou says that while the task the robot performs is similar to work done by robots in other industries, the “special difficulty” students faced while designing the Oystamaran was its work environment.
“You have a floating device, which must be self-propelled, and which must find these objects in an environment that is not neat,” Triantafyllou says. “It’s a combination of vision and navigation in an environment that changes, with currents, wind, and waves. Very quickly, it becomes a complicated task.”
Kornberg, who had constructed the original central flipping mechanism and the basic structure of the vessel as a staff member at MIT Sea Grant after graduating in May 2020, worked as a lab instructor for the next capstone class related to the project in spring 2021. Andrew Bennett, education administrator at MIT Sea Grant, co-taught that class, in which students designed an Oystamaran version 2.0, which was tested at Ward Aquafarms and managed to flip several rows of bags while being controlled remotely. Next steps will involve making the vessel more autonomous, so it can be launched, navigate autonomously to the oyster bags, flip them, and return to the launching point. A third capstone class related to the project will take place this spring.
The students operate the “Oystamaran” robot remotely from the boat. Image: John Freidah, MIT MechE
Bennett says an ideal project outcome would be, “We have proven the concept, and now somebody in industry says, ‘You know, there’s money to be made in oysters. I think I’ll take over.’ And then we hand it off to them.”
Meanwhile, he says an unexpected challenge arose with getting the Oystamaran to go between tightly packed rows of oyster bags in the center of an array.
“How does a robot shimmy in between things without wrecking something? It’s got to wiggle in somehow, which is a fascinating controls problem,” Bennett says, adding that the problem is a source of excitement, rather than frustration, to him. “I love a new challenge, and I really love when I find a problem that no one expected. Those are the fun ones.”
Triantafyllou calls the Oystamaran “a first for the industry,” explaining that the project has demonstrated that robots can perform extremely useful tasks in the ocean, and will serve as a model for future innovations in aquaculture.
“Just by showing the way, this may be the first of a number of robots,” he says. “It will attract talent to ocean farming, which is a great challenge, and also a benefit for society to have a reliable means of producing food from the ocean.”
By Marco Arruda
In this post you’ll learn how to program a robot to avoid obstacles using ROS2 and C++. Up to the end of the post, the Dolly robot moves autonomously in a scene with many obstacles, simulated using Gazebo 11.
You’ll learn:
Before anything else, make sure you have the rosject from the previous post, you can copy it from here.
Launch the simulation in one webshell and in a different tab, checkout the topics we have available. You must get something similar to the image below:
In order to have our obstacle avoidance algorithm, let’s create a new executable in the file ~/ros2_ws/src/my_package/obstacle_avoidance.cpp:
#include "geometry_msgs/msg/twist.hpp" // Twist
#include "rclcpp/rclcpp.hpp" // ROS Core Libraries
#include "sensor_msgs/msg/laser_scan.hpp" // Laser Scan
using std::placeholders::_1;
class ObstacleAvoidance : public rclcpp::Node {
public:
ObstacleAvoidance() : Node("ObstacleAvoidance") {
auto default_qos = rclcpp::QoS(rclcpp::SystemDefaultsQoS());
subscription_ = this->create_subscription(
"laser_scan", default_qos,
std::bind(&ObstacleAvoidance::topic_callback, this, _1));
publisher_ =
this->create_publisher("cmd_vel", 10);
}
private:
void topic_callback(const sensor_msgs::msg::LaserScan::SharedPtr _msg) {
// 200 readings, from right to left, from -57 to 57 degress
// calculate new velocity cmd
float min = 10;
for (int i = 0; i < 200; i++) { float current = _msg->ranges[i];
if (current < min) { min = current; } }
auto message = this->calculateVelMsg(min);
publisher_->publish(message);
}
geometry_msgs::msg::Twist calculateVelMsg(float distance) {
auto msg = geometry_msgs::msg::Twist();
// logic
RCLCPP_INFO(this->get_logger(), "Distance is: '%f'", distance);
if (distance < 1) {
// turn around
msg.linear.x = 0;
msg.angular.z = 0.3;
} else {
// go straight ahead
msg.linear.x = 0.3;
msg.angular.z = 0;
}
return msg;
}
rclcpp::Publisher::SharedPtr publisher_;
rclcpp::Subscription::SharedPtr subscription_;
};
int main(int argc, char *argv[]) {
rclcpp::init(argc, argv);
rclcpp::spin(std::make_shared());
rclcpp::shutdown();
return 0;
}
In the main function we have:
Inside the class constructor:
The obstacle avoidance intelligence goes inside the method calculateVelMsg. This is where decisions are made based on the laser readings. Notice that is depends purely on the minimum distance read from the message.
If you want to customize it, for example, consider only the readings in front of the robot, or even check if it is better to turn left or right, this is the place you need to work on! Remember to adjust the parameters, because the way it is, only the minimum value comes to this method.
This executable depends on both geometry_msgs and sensor_msgs, that we have added in the two previous posts of this series. Make sure you have them at the beginning of the ~/ros2_ws/src/my_package/CMakeLists.txt file:
# find dependencies find_package(ament_cmake REQUIRED) find_package(rclcpp REQUIRED) find_package(geometry_msgs REQUIRED) find_package(sensor_msgs REQUIRED)
And finally, add the executable and install it:
# obstacle avoidance
add_executable(obstacle_avoidance src/obstacle_avoidance.cpp)
ament_target_dependencies(obstacle_avoidance rclcpp sensor_msgs geometry_msgs)
...
install(TARGETS
reading_laser
moving_robot
obstacle_avoidance
DESTINATION lib/${PROJECT_NAME}/
)
Compile the package:
colcon build --symlink-install --packages-select my_package
In order to run, use the following command:
ros2 run my_package obstacle_avoidance
It will not work for this robot! Why is that? We are subscribing and publishing to generic topics: cmd_vel and laser_scan.
We need a launch file to remap these topics, let’s create one at ~/ros2_ws/src/my_package/launch/obstacle_avoidance.launch.py:
from launch import LaunchDescription
from launch_ros.actions import Node
def generate_launch_description():
obstacle_avoidance = Node(
package='my_package',
executable='obstacle_avoidance',
output='screen',
remappings=[
('laser_scan', '/dolly/laser_scan'),
('cmd_vel', '/dolly/cmd_vel'),
]
)
return LaunchDescription([obstacle_avoidance])
Recompile the package, source the workspace once more and launch it:
colcon build --symlink-install --packages-select my_package
source ~/ros2_ws/install/setup.bash
ros2 launch my_package obstacle_avoidance.launch.py
The post Exploring ROS2 with a wheeled robot – #4 – Obstacle Avoidance appeared first on The Construct.
AI-designed (C-shaped) organisms push loose stem cells (white) into piles as they move through their environment. Credit: Douglas Blackiston and Sam Kriegman
By Joshua Brown, University of Vermont Communications
To persist, life must reproduce. Over billions of years, organisms have evolved many ways of replicating, from budding plants to sexual animals to invading viruses.
Now scientists at the University of Vermont, Tufts University, and the Wyss Institute for Biologically Inspired Engineering at Harvard University have discovered an entirely new form of biological reproduction—and applied their discovery to create the first-ever, self-replicating living robots.
The same team that built the first living robots (“Xenobots,” assembled from frog cells—reported in 2020) has discovered that these computer-designed and hand-assembled organisms can swim out into their tiny dish, find single cells, gather hundreds of them together, and assemble “baby” Xenobots inside their Pac-Man-shaped “mouth”—that, a few days later, become new Xenobots that look and move just like themselves.
And then these new Xenobots can go out, find cells, and build copies of themselves. Again and again.
“With the right design—they will spontaneously self-replicate,” says Joshua Bongard, Ph.D., a computer scientist and robotics expert at the University of Vermont who co-led the new research.
The results of the new research were published in the Proceedings of the National Academy of Sciences.
In a Xenopus laevis frog, these embryonic cells would develop into skin. “They would be sitting on the outside of a tadpole, keeping out pathogens and redistributing mucus,” says Michael Levin, Ph.D., a professor of biology and director of the Allen Discovery Center at Tufts University and co-leader of the new research. “But we’re putting them into a novel context. We’re giving them a chance to reimagine their multicellularity.” Levin is also an Associate Faculty member at the Wyss Institute.
As Pac-man-shaped Xenobot “parents” move around their environment, they collect loose stem cells in their “mouths” that, over time, aggregate to create “offspring” Xenobots that develop to look just like their creators. Credit: Doug Blackiston and Sam Kriegman
And what they imagine is something far different than skin. “People have thought for quite a long time that we’ve worked out all the ways that life can reproduce or replicate. But this is something that’s never been observed before,” says co-author Douglas Blackiston, Ph.D., the senior scientist at Tufts University and the Wyss Institute who assembled the Xenobot “parents” and developed the biological portion of the new study.
“This is profound,” says Levin. “These cells have the genome of a frog, but, freed from becoming tadpoles, they use their collective intelligence, a plasticity, to do something astounding.” In earlier experiments, the scientists were amazed that Xenobots could be designed to achieve simple tasks. Now they are stunned that these biological objects—a computer-designed collection of cells—will spontaneously replicate. “We have the full, unaltered frog genome,” says Levin, “but it gave no hint that these cells can work together on this new task,” of gathering and then compressing separated cells into working self-copies.
“These are frog cells replicating in a way that is very different from how frogs do it. No animal or plant known to science replicates in this way,” says Sam Kriegman, Ph.D., the lead author on the new study, who completed his Ph.D. in Bongard’s lab at UVM and is now a post-doctoral researcher at Tuft’s Allen Center and Harvard University’s Wyss Institute for Biologically Inspired Engineering.
On its own, the Xenobot parent, made of some 3,000 cells, forms a sphere. “These can make children but then the system normally dies out after that. It’s very hard, actually, to get the system to keep reproducing,” says Kriegman. But with an artificial intelligence program working on the Deep Green supercomputer cluster at UVM’s Vermont Advanced Computing Core, an evolutionary algorithm was able to test billions of body shapes in simulation—triangles, squares, pyramids, starfish—to find ones that allowed the cells to be more effective at the motion-based “kinematic” replication reported in the new research.
“We asked the supercomputer at UVM to figure out how to adjust the shape of the initial parents, and the AI came up with some strange designs after months of chugging away, including one that resembled Pac-Man,” says Kriegman. “It’s very non-intuitive. It looks very simple, but it’s not something a human engineer would come up with. Why one tiny mouth? Why not five? We sent the results to Doug and he built these Pac-Man-shaped parent Xenobots. Then those parents built children, who built grandchildren, who built great-grandchildren, who built great-great-grandchildren.” In other words, the right design greatly extended the number of generations.
Kinematic replication is well-known at the level of molecules—but it has never been observed before at the scale of whole cells or organisms.
An AI-designed “parent” organism (C shape; red) beside stem cells that have been compressed into a ball (“offspring”; green). Credit: Douglas Blackiston and Sam Kriegman
“We’ve discovered that there is this previously unknown space within organisms, or living systems, and it’s a vast space,” says Bongard. “How do we then go about exploring that space? We found Xenobots that walk. We found Xenobots that swim. And now, in this study, we’ve found Xenobots that kinematically replicate. What else is out there?”
Or, as the scientists write in the Proceedings of the National Academy of Sciences study: “life harbors surprising behaviors just below the surface, waiting to be uncovered.”
Some people may find this exhilarating. Others may react with concern, or even terror, to the notion of a self-replicating biotechnology. For the team of scientists, the goal is deeper understanding.
“We are working to understand this property: replication. The world and technologies are rapidly changing. It’s important, for society as a whole, that we study and understand how this works,” says Bongard. These millimeter-sized living machines, entirely contained in a laboratory, easily extinguished, and vetted by federal, state and institutional ethics experts, “are not what keep me awake at night. What presents risk is the next pandemic; accelerating ecosystem damage from pollution; intensifying threats from climate change,” says UVM’s Bongard. “This is an ideal system in which to study self-replicating systems. We have a moral imperative to understand the conditions under which we can control it, direct it, douse it, exaggerate it.”
Bongard points to the COVID epidemic and the hunt for a vaccine. “The speed at which we can produce solutions matters deeply. If we can develop technologies, learning from Xenobots, where we can quickly tell the AI: ‘We need a biological tool that does X and Y and suppresses Z,’ —that could be very beneficial. Today, that takes an exceedingly long time.” The team aims to accelerate how quickly people can go from identifying a problem to generating solutions—”like deploying living machines to pull microplastics out of waterways or build new medicines,” Bongard says.
“We need to create technological solutions that grow at the same rate as the challenges we face,” Bongard says.
And the team sees promise in the research for advancements toward regenerative medicine. “If we knew how to tell collections of cells to do what we wanted them to do, ultimately, that’s regenerative medicine—that’s the solution to traumatic injury, birth defects, cancer, and aging,” says Levin. “All of these different problems are here because we don’t know how to predict and control what groups of cells are going to build. Xenobots are a new platform for teaching us.”
The scientists behind the Xenobots participated in a live panel discussion on December 1, 2021 to discuss the latest developments in their research. Credit: Wyss Institute at Harvard University