&Bullet; physics 14, 38

Using a swarm of puck-shaped robots, the researchers simulate interactions between biological organisms and their environment.

L. Liu / Chongqing Univ.

Robotic herd. Disc-shaped robots are programmed to move towards light and mimic the foraging behavior of bacteria and other organisms. (See the video below.)

A swarm of small robots can mimic deer or bacteria foraging for food. The researchers published these electronic collectors on an LED screen whose light output was adjusted to mimic the amount of a consumable resource – like grass for deer [1] . As soon as the robots had “eaten” the resource in one place, they left the exhausted region. This programmed behavior resulted in collective patterns that resembled phases of matter, including liquid, crystal, and glass. The team plans to further explore the robots’ self-organization by giving them additional properties, such as a preference for the consumption of certain colors of light.

“Active matter” refers to systems made up of many elements that move on their own. Biological examples are bacterial colonies and flocks of birds that researchers try to imitate with artificial active ingredients. Earlier experiments used robots or self-propelled particles that move in response to a physical input such as light or chemical concentrations (see Focus: Sensing Delays Control Robot Swarming). But in a real ecosystem, the environment is not a fixed laboratory table – it is constantly changing in response to the activity of organisms. And the organisms have to adapt to the changes they bring about in their environment. Bacteria, for example, consume nutrients and thus change the concentration of chemicals in their environment. “You can feel that you are in a depleted region and you are moving towards more food,” says Robert Austin of Princeton University.

It is possible to design computer simulations that incorporate hungry organisms living in an environment with limited resources. Austin believes, however, that simulations cannot capture the collective or “emergent” behavior that arises in biologically active matter. “Biological objects can be of a complexity that a digital computer cannot simulate,” says Austin. He has teamed up with Liyu Liu of Chongqing University in China and other colleagues to develop an analog experiment that biology-inspired robots can use to explore and shape their environment.

The collective behavior of “light-eating” robots is explored in a shrinking environment. In the beginning the robots move randomly, like in a gas. They finally form a crystal-like hexagonal pattern, with each robot “dancing” with six partners in a ring. Towards the end, the shrunken “dance floor” darkens – due to the robot’s light consumption – and the movement becomes fluid and then glass-like. The video is accelerated by

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.The collective behavior of “light-eating” robots is explored in a shrinking environment. In the beginning the robots move randomly, like in a gas. They eventually form a crystal-like hexagonal pattern, with each robot “dancing” with six partners … show more

The team fabricated 8 cm wide hockey puck-shaped robots with wheels and light sensors on the underside. The robots were programmed to move in response to the light from below, always heading for brighter light, in the direction in which the brightness increased the fastest. The researchers placed 50 of these robots on one

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LED light board, the pixels of which can be controlled individually. The lights under each robot have been dimmed over a specific spatial area and for a selected length of time to mimic how a resource is consumed and later restored. The robots responded to the dimming by rolling in the brightest direction and then dimming their new locations. The team calls the gradient-guided movement of the robots a “warp drive” because it is based on a twisting of the “resource space” around a robot [2] .

The team showed that the robots repel each other as they are attracted to bot-free regions where there is more “food”. The researchers examined the effects of these interactions by creating a circular light environment surrounded by darkness. In the beginning the circle was big and the robots moved randomly like atoms in a gas. But when the researchers made the area smaller by reducing the diameter of the circle, the robots packed up into a hexagonal crystalline pattern, with each bot confined to a small patch surrounded by six neighbors. With further shrinking, the exhausted regions began to overlap and formed a uniform light landscape in which no region was more attractive than the other. This smoothing of the landscape “melted” the crystal into a liquid-like phase, followed by a disordered, glass-like phase in which the robots locked themselves on contact with one another.

Austin says this emergent behavior was not observed in previous experiments with active matter. He hopes that upgrades to the robots could reveal biologically relevant behavior. To this end, his team has started to include color sensitivity, with some robots looking for red light, for example, while others consuming blue light. These color features could then be passed from one robot to another in a robotic version of the reproduction. The researchers imagine that these gene-bearing robots could, for example, provide insights into the development of tumor cells. “Robotic matter is a vacant land full of potential,” says Liu.

“The novelty of this work lies in the clever and simple experimental implementation, in which both the grazing rate and regrowth are controlled,” says active matter physicist Sriram Ramaswamy from the Indian Institute of Science in Bengaluru, India. He believes the system offers many potential directions for exploration, such as studying how the environment can go through its own phase transitions in response to interactions with the community of organisms.


–Michael Schirber

Michael Schirber is the corresponding editor for physics based in Lyon, France.

References

  1. G. Wang et al., “Emerging field-controlled robot swarm states”, Phys. Rev. Lett.126, 108002 (2021).
  2. T. Phan et al., “Bootstrapped movement of an agent in an adaptive resource landscape”, symmetry13, 225 (2021).

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