November 29, 2004 Scientists at the Dartmouth Robotics Lab have developed the first reliable methods to produce self-configurable robots capable of controlling their shape according to the task at hand and environment they are in. Based on a 'lattice' of autonomous units linked into a networked organism, the breakthrough promises a new generation of self-transforming robots that can perform a variety of different tasks without human intervention.

Robots are usually designed to perform one task very well, whether it's assembling parts in a factory or vacuuming the living room. But ask those robots to perform another task or even the same task in a new environment, and you're asking for trouble.


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Self-reconfigurable robots, on the other hand, can reshape themselves as their task or environment changes, ideally without human intervention. A walking robot used for search-and-rescue operations would transform into a snake-like form to slither through small spaces in a collapsed building. A rolling robot exploring the surface of Mars would flow like water over a vertical drop or "flow" uphill onto a rock ledge.

Today's state-of-the-art shape-shifting robots are a long way from living up to that vision, however. Daniela Rus and colleagues at the Dartmouth Robotics Lab in the US have made advances on both the mechanical and control fronts. On the mechanical side, she pioneered the design of 3-D shape-shifting robots built out of "expanding cubes," such as the Crystal modules.

Each Crystal module, or "atom," has sides that extend and contract and that use a 'key-in-lock' mechanism to attach to neighbouring atoms. The expanding-cube concept is an example of so-called "lattice robots," which can assume a wide variety of 3-D shapes, an advantage over robots whose modules can only form long, thin chains.

Shape-shifting for such lattice robots boils down to exercises in control and planning, which happen at two levels. At one level, the robot must plan how to remodel itself from shape A to shape B. At another level, the robot must also plan the series of shapes needed to accomplish more complicated tasks, such as moving over rough terrain.

Early work in self-reconfiguring robots used centralised methods to control how the pieces reassembled themselves. Today, researchers in the field generally acknowledge the need for distributed methods, in which each robotic module takes at least some control of its own destiny.

"Since we are talking about potentially very large systems, with thousands of individual parts, it's important to consider distributed control and planning," Rus said. "And parallel and distributed algorithms are hard to guarantee."

A recent paper in the September 2004 issue of the International Journal of Robotics Research (IJRR) provides some of the first distributed methods for generating provably correct steps for both types of control and planning. In other words, robots that reconfigure themselves using these plans won't fall to pieces, in a very literal sense, or get irreversibly stuck as they move from place to place.

The paper presents sets of about a dozen rules that instruct lattice robots how to roam over terrain, build tall structures to overcome obstacles or enter closed spaces through small tunnels. Rus and her colleagues analysed the simpler rule sets for correctness and developed automated methods to prove that the more complicated ones worked. More complex tasks, however, demand more complicated rule sets, and Rus is now investigating ways that would allow robots to learn their own rules.

In addition to the theoretical guarantees, the papers represent a departure from another norm. Often in robotics, a control method is tied to specific hardware, making it more difficult to apply lessons from one robot system to another. Rus's work applies to control and planning for the entire class of lattice robots, of which the Crystal atoms are one example.

"The [latest IJRR] paper is an example of a methodology for developing and proving algorithms and understanding control systems in general," Rus said. "It's important to learn more general lessons. You get a deeper sense about the self-reconfiguration problem."

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