New autonomous robotic construction crew to build towers

An Indian-origin scientist at Harvard has developed an army of autonomous termite-like robots that can cooperate to build complex structures such as towers and pyramids without any supervision.
Inspired by the termites' resilience and collective intelligence, the system needs no supervisor, no eye in the sky, and no communication: just simple robots that cooperate by modifying their environment, researchers said.
The TERMES system developed by scientists at Harvard University demonstrates that collective systems of robots can build complex, three-dimensional structures without the need for any central command or prescribed roles.
The TERMES robots can build towers, castles, and pyramids out of foam bricks, autonomously building themselves staircases to reach the higher levels and adding bricks wherever they are needed.

In the future, similar robots could lay sandbags in advance of a flood, or perform simple construction tasks on Mars, researchers said.
"The key inspiration we took from termites is the idea that you can do something really complicated as a group, without a supervisor, and secondly that you can do it without everybody discussing explicitly what's going on, but just by modifying the environment," said principal investigator Radhika Nagpal, Fred Kavli Professor of Computer Science at Harvard School of Engineering and Applied Sciences (SEAS).

Each robot executes its building process in parallel with others, but without knowing who else is working at the same time.

If one robot breaks, or has to leave, it does not affect the others. This also means that the same instructions can be executed by five robots or five hundred.
The TERMES system is an important proof of concept for scalable, distributed artificial intelligence.
Nagpal's Self-Organising Systems Research Group specialises in distributed algorithms that allow very large groups of robots to act as a colony.
When many agents get together - whether they're termites, bees, or robots - often some interesting, higher-level behaviour emerges that you wouldn't predict from looking at the components by themselves, said researchers.
The results of the four-year project are published in the journal Science.