In natural ecosystems, the herd mentality plays a major role – from schools of fish to beehives, to ant colonies. This collective behaviour allows the whole to exceed the sum of its parts and better respond to threats and challenges.
In their new work, the researchers engineered social interactions among tiny machines, so that they could act as one coordinated group, performing tasks better than they would if they were moving as individuals or at random.
“All these groups, flocks of birds, schools of fish, and others, each member of the group has this natural inclination to work in concert with its neighbour, and together, they are smarter, stronger, and more efficient than they would be on their own,” says Yuebing Zheng, Associate Professor in the mechanical engineering department at the University of Texas at Austin and the Texas Materials Institute.
“We wanted to learn more about the mechanisms that make this happen and see if we can reproduce it.”
Zheng and his team have given these swarms a new trait called ‘adaptive time delay’. This concept allows each microrobot within the swarm to adapt its motion to changes in local surroundings.
By doing this, the swarm showed a significant increase in responsivity without decreasing its robustness – the ability to quickly respond to any environmental change while maintaining the integrity of the swarm.
This finding builds on a novel optical feedback system – the ability to direct these microrobots in a collective way using controllable light patterns.
The adaptive time delay strategy offers potential for scalability and integration into larger machinery. This approach could significantly enhance the operational efficiency of autonomous drone fleets.
Similarly, it could enable conveys of trucks and cars autonomously to navigate extensive highway journeys in unison, with improved responsiveness and increased robustness.
In the same way that schools of fish can communicate and follow each other, so will these machines. As a result, there’s no need for any kind of central control, which takes more data and energy to operate.
“Nanorobots, on an individual basis, are vulnerable to complex environments; they struggle to navigate effectively in challenging conditions such as bloodstreams or polluted waters,” says co-author Zhihan Chen, a PhD student in Zheng’s lab.
“This collective motion can help them better navigate a complicated environment and reach the target efficiently and avoid obstacles or threats.”
Having proven this swarm mentality in the lab setting, the next step is to introduce more obstacles. These experiments were conducted in a static liquid solution. Up next, they’ll try to repeat the behaviour in flowing liquid. And then they’ll move to replicate it inside an organism.
Once fully developed, these smart swarms could serve as advanced drug delivery forces, able to navigate the human body and elude its defences to bring medicine to its target. Or they could operate like iRobot robotic vacuums, but for contaminated water, collectively cleaning every bit of an area together.