Robot fish illustrate 'collective intelligence' at work in nature
One for all, and all for one.
They are one of nature's most spectacular and most mysterious spectacles — moments where hundreds if not thousands of animals, herrings, starlings, beetles, appear to move together seemingly as one organism. This complex behavior is as harmonious as it is evolutionary advantageous.
But whether it is a murmuration of starlings, or a school of fish, roboticists have tried for years to capture and mimic this behavior in machines. They have had some success in building swarming robots on land, but it has proven more difficult to recreate this phenomenon in water. Until now.
A team of roboticists from Harvard has designed a school of miniature robot fish, which they dub Bluebots, that can mimic natural swarming behavior in a laboratory pool. To move as one, the bots use camera-eyes and LED lights to track one another, allowing for a similarly coordinated behavior as one might expect from a giant school of silvery mackerel coursing through the ocean.
Beyond this remarkable achievement, however, the research reveals something quite unexpected: new information about how these moments of 'collective intelligence' occur in nature.
Why it matters — Florian Berlinger, a postdoctoral candidate in computer science at Harvard University and first author on the paper, told Science Robotics that these robots can help scientists study the nature of collective intelligence in ways traditional ecology studies can not.
"With this research, we can not only build more advanced robot collectives, but also learn about collective intelligence in nature," Berlinger says. "Fish must follow even simpler behavior patterns when swimming in schools than our robots do. This simplicity is so beautiful yet hard to discover."
The robots could also reveal more about the world's oceans at large — perhaps the last true frontier in terms of on-Earth exploration is the deep ocean. Small underwater robots that can move like fish would be both cheaper and less disruptive to other creatures during deep-sea research.
The study's findings were published Wednesday in the journal Science Robotics.
Here's the background — While the Bluebots are not novel in their swarming behavior, the researchers say there is an important distinction between the sensors similar robots have used for their motion and those used in this design.
Older swarming robots have often relied on externally collected data to make their moves, like GPS tags on individual bots. While effective, this limits their ability to transfer to use in water, where such signals can become garbled.
For a more robust design, researchers turned to fish themselves for inspiration in the form of bioluminescence.
"To rapidly detect members of their school, many such species have evolved specialized visual patterns (e.g., “schooling marks” and prominent stripes), and nighttime schooling fish, such as the flashlight fish Anomalops katoptron, exploit individual bioluminescence. Inspired by these natural systems," explain the authors in their study.
"Bluebot achieves 3D vision and neighborhood sensing using a combination of cameras and blue-light light-emitting diodes (LEDs)."
In their work, the researchers set out to demonstrate how these Bluebots could display complex, coordinated behavior with only these two rudimentary sensors.
How they did it — To test this theory, the researchers plopped their school of seven Bluebots (which are less than two meters in length) into a freshwater tank in their lab and tasked them with demonstrating three types of coordinated behavior:
- Controlled dispersion
- Dynamic motion, or milling in a circle
In addition to their blue LEDs and camera eyes, these robot fish (the design of which was inspired by the surgeonfish, of Finding Nemo fame) swam through the water with electromagnetically driven dorsal, pectoral, and caudal fins to achieve these different movement patterns.
As for how they calculated their movements, the Bluebots calculated one another relative distance and angular position every half-second using a machine-vision algorithm.
What they discovered — While the Bluebots didn't perform perfectly (there were occasional collisions or instances of mistaking their fishy reflection for a fellow Bluebot), overall the researchers report these bots were able to successfully mimic biological swarming patterns using only visual data.
In practice, these movement patterns involved huddling together at the center of the tank, or dispersing all at once (to imitate predator evasion), as well as a process called "milling," where fish swim in a coordinated circle or funnel to avoid predators.
"By focusing on a minimalist form of visual coordination, we were able to achieve versatility and demonstrate programmability for an underwater robot swarm," write the authors.
What's next — Despite their successes, the authors acknowledge that there are quite a few limitations and bugs to iron out before these robots would be ready for the open ocean.
A companion essay, also published in Science Robotics by scientists not associated with the research, questioned how natural water flow, light variation, and even vegetation obstruction, would affect these robots' capabilities in a less controlled underwater environment, like a lake or ocean.
Better quality cameras and on-board electronics could help solve this problem, write Berlinger and colleagues, but adding additional sensor types could also be a solution.
Ultimately, Berlinger and colleagues are encouraged by how robots like these could improve research missions deep under the sea, from coral reefs to sunken ships.
"The underwater domain is still largely unexplored in general, but especially when it comes to robotics where many more solutions exist for operations on land and in air," said Berlinger.
"With our implicit and minimalistic approach for inter-robot coordination, we present an alternative pathway for robust and adaptive underwater swarm operations."
Abstract: Many fish species gather by the thousands and swim in harmony with seemingly no effort. Large schools display a range of impressive collective behaviors, from simple shoaling to collective migration and from basic predator evasion to dynamic maneuvers such as bait balls and flash expansion. A wealth of experimental and theoretical work has shown that these complex three-dimensional (3D) behaviors can arise from visual observations of nearby neighbors, without explicit communication. By contrast, most underwater robot collectives rely on centralized, above-water, explicit communication and, as a result, exhibit limited coordination complexity. Here, we demonstrate 3D collective behaviors with a swarm of fish-inspired miniature underwater robots that use only implicit communication mediated through the production and sensing of blue light. We show that complex and dynamic 3D collective behaviors—synchrony, dispersion/aggregation, dynamic circle formation, and search-capture—can be achieved by sensing minimal, noisy impressions of neighbors, without any centralized intervention. Our results provide insights into the power of implicit coordination and are of interest for future underwater robots that display collective capabilities on par with fish schools for applications such as environmental monitoring and search in coral reefs and coastal environments.